PhD
International PhD Fellowships in European Union Law at the University of Southern Denmark
The University of Southern Denmark is pleased to announce the International PhD Fellowships in European Union Law at the University of Southern Denmark for the brilliant students for the academic year 2023-2024.
The bursary is available for international candidates in order to pursue a PhD degree program at the University of Southern Denmark
The University of Southern Denmark, founded in 1998, provides a wide array of educational opportunities with over 115 study programs available at the Bachelor’s, Master’s, and PhD levels. The university offers internationally recognized degree programs under five faculties, namely Social Sciences, Humanities, Engineering, Science, and Health.
Why study at the University of Southern Denmark? The University of Southern Denmark (SDU) provides exceptional research and development facilities, fostering flexible working environments and a conducive atmosphere for research. The university maintains strong connections with knowledgeable researchers, ensuring a collaborative and enriching research experience.
Eligibility
- Eligible Countries: Applications are welcomed from all around the world.
- Acceptable Course or Subjects: The position will be awarded for PhD for a 3-year position as PhD in the area of Power Capacitors and their applications.
- Admissible Criteria: To be eligible, the applicants must meet all the following:
- Applicants should hold a cand.jur (LL.M.), cand.merc.jur, or other form of postgraduate legal education. If candidates have not completed their legal education yet, they should be able to demonstrate proof of being about to complete it. Records of any published research prior to the application may be regarded as an advantage, and experience outside of legal research may be considered relevant for candidates’ applications. However, the combined contents of the application, enthusiasm, and determination of the candidate will be taken into consideration in order to find the best match for the position/team
Location :Denmark
Closing Date :15th August 2023.
HOW TO APPLY FOR THE SCHOLARSHIP
Click Here to Submit your Application on the University Website
International PhD Fellowships in Theoretical Reinforcement Learning-Denmark
The University of Copenhagen is thrilled to announce International PhD Fellowships in Theoretical Reinforcement Learning in Denmark.
The study program is open for both domestic and international applicants who want to pursue a PhD degree program at the university.
The University of Copenhagen is not only the most prestigious educational establishment in Denmark but also a public research university located in the capital city of Copenhagen. It is the second-oldest university in Scandinavia, after Uppsala University, and it was established in 1479. It is now placed 37th on the list of the Best Global Universities.
Why choose to study at the University of Copenhagen? The University of Copenhagen is a research institution of world-class calibre with the mission of preparing its students for a diverse array of jobs in both the public and private sectors. To this end, the university works ceaselessly and methodically to ensure that its higher education is accessible to all students by implementing strategic initiatives and making daily efforts to improve its quality. Intellectual originality and analytical vigilance are the driving forces behind the university.
Eligibility
- Eligible Countries: Students from Denmark and other foreign countries are both eligible to apply.
- Eligible Course or Subjects: Students can apply for a PhD programme at the Department of Computer Science’s Faculty of Science at the University of Copenhagen.
- Eligibility Criteria: To be eligible for the regular PhD programme, you must have completed a degree programme, equivalent to a Danish master’s degree (180 ECTS/3 FTE BSc + 120 ECTS/2 FTE MSc) related to the subject area of the project, e.g. Computer Science/Engineering, Electrical Engineering. For information of eligibility of completed programmes, see General assessments for specific countries and Assessment database.
Location :Denmark
Closing Date :16 July 2023.
HOW TO APPLY FOR THE SCHOLARSHIP
Click Here to Submit your Application on the University Website
Short Stay Research Fellowships for International Students in Belgium
Africa Platform of Ghent University Association (GAP) is inviting outstanding applicants to apply for Short Stay Research Fellowships for International Students. The grant is available for the academic session 2023-2024
The studentship is designed to give for international students who have a chance to study a research degree in Belgium and aims to help them with covering some of their study expenses.
Ghent University is ranked among the top 100 universities in the world and is one of the most prestigious educational institutions in Belgium. Our 11 faculties combine to provide more than 200 different academic classes, and in-depth research is carried out in a wide variety of scientific subfields. Ghent institution’s Global Campus is also the first European institution to be established in Songdo, which is located in South Korea.
Why study at Ghent University? Ghent is often regarded as one of the most desirable locations in all of Belgium for academic pursuits by students from other countries. According to its performance across a set of generally acknowledged benchmarks of academic success, it has earned the 95th spot on the list of the finest universities in the world.
Eligibility
- Eligible Countries:
- Cluster 1: Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Congo, Côte d’Ivoire, DR Congo, Egypt, Equatorial Guinea, Gabon, the Gambia, Ghana, Guinea, Guinea-Bissau, Liberia, Libya, Mali, Mauretania, Morocco, Niger, Nigeria, Senegal, Sierra Leone, South Sudan, Sudan, Togo, Tunisia, Western-Sahara;
- Cluster 2: Angola, Botswana, Burundi, Djibouti, Eritrea, Eswatini, Ethiopia, Kenya, Lesotho, Madagascar, Malawi, Mozambique, Namibia, Rwanda, Somalia, South Africa, Tanzania, Uganda, Zambia, Zimbabwe.
- Acceptable Course or Subjects: The scholarship will be awarded to a research degree in any subject offered by the university.
- Admissible Criteria: To be eligible, the applicants must meet all the following criteria:
- You have a Master’s degree in Veterinary Medicine, final year students can also apply.
- You feel like working in a team, in collaboration with your supervisors Prof. Siska Croubels and Prof. Mathias Devreese (Laboratory for Pharmacology and Toxicology), Prof. Gunther Antonissen (Poultry Health Sciences Chair holder), Prof. Jeroen Dewulf (Veterinary Epidemiology Unit) ), practice veterinarians, doctoral students and the laboratory staff.
- You have a problem-solving, organized, critical and result-oriented attitude.
- You have interpersonal skills, empathy, flexibility and a critical mind to function in a multidisciplinary environment.
- You have communication skills such as dealing with poultry farmers and veterinary practice (company visits), conducting substantive discussions, scientific reporting and presenting in both Dutch and English.
Location :Belgium
Closing Date :1 October 2023.
HOW TO APPLY FOR THE SCHOLARSHIP
Click Here to Submit your Application on the University Website
PhD Student in Neuroscience (Ala-Laurila Lab, Finland) at Aalto University
We are looking for an excellent PhD Candidate to work in characterization of healthy and diseased mouse models from retinal circuits to visually guided behaviour
Timeline of the call:
The position will be filled as soon as a suitable candidate is found. Thus, submit your application as soon as possible. In any case we will fill the position by the spring of the year 2023.
Objectives:
- Characterize the retinal output of different mouse models of retinal diseases
- Correlate the retinal output to the animal behavior in the water maze
Host institution: Aalto University (Aalto, Finland) – Supervisor: Petri Ala-Laurila
Dr Ala-Laurila has two laboratories (http://ala-laurila.biosci.helsinki.fi/): one at Aalto University and one at University of Helsinki. Both labs work in integrated collaboration and have state-of-the art approaches to study retinal circuits and visually-guided behavior. They combine cutting-edge electrophysiological recording techniques with precise manipulations of retinal circuit function, mathematical modelling and quantitative behavioral measurements: https://www.youtube.com/watch?v=ZniIYFSIcT8
Aalto University (Aalto) is the largest technology-oriented university in Finland. The Department of Neuroscience and Biomedical Engineering is located in the Otaniemi campus – one of the most important north-european technology hubs, with a high concentration of companies, and research institutes from the high-technology sector, and with a thriving culture of entrepreneurship.
The University of Helsinki is Finland’s largest and oldest academic institution and an innovative centre of science and thinking. University of Helsinki has contributed since 1640 to the establishment of a fair and equal society that is considered one of the best in the world. The multidisciplinary academic community solves problems that affect us all – with the power of knowledge, for the world.
Requirements from the applicant:
- Highly motivated, previous experience on behavioral experiments and/or electrophysiology is a plus
- Formal requirements for eligibility: at the time of appointment, applicant must not have resided or carried out their main activity (work, studies, etc.) in Finland for more than 12 months in the 3 years immediately prior to their recruitment. Short stays, such as holidays, are not taken into account.
Application documents:
- Motivation letter
- CV
- Recommendation letters (max. 3)
- Scanned copy of the degree which would formally entitle the candidate to embark on doctorate.
- Transcript of records for both Bachelor´s degree and Master´s degree (or equivalents).
Applications will be reviewed as soon as they arrive so please submit your application as soon as you can.
For additional information, please contact Krishna Dovzhik (Ala-Laurila lab), [email protected]
Location : Lämpömiehenkuja 2 Helsinki, Finland
Closing Date : 2023-05-31
HOW TO APPLY
Click Here to Submit your Application on the University Website
PhD in Machine Learning: Theoretically motivated deep learning at KTH Royal Institute of Technology Sweden
School of Electrical Engineering and Computer Science at KTH
KTH Royal Institute of Technology in Stockholm has grown to become one of Europe’s leading technical and engineering universities, as well as a key centre of intellectual talent and innovation. We are Sweden’s largest technical research and learning institution and home to students, researchers and faculty from around the world. Our research and education covers a wide area including natural sciences and all branches of engineering, as well as architecture, industrial management, urban planning, history and philosophy.
Project description
At the Division of Computational Science and Technology at KTH we are seeking a new PhD student in Machine Learning / Computer Vision to handle scale-dependent information in image data.
In our research, we develop deep networks for processing image data that handle scaling transformations and other image transformations in a theoretically well-founded manner. Our research in this area comprises both theoretical modelling of the influence of image transformations on different architectures for deep networks as well as experimental evaluations of such networks on benchmark datasets to explore their properties. The work also comprises the creation of new benchmark datasets, to enable characterization of properties of deep networks that are not covered by existing datasets.
For examples of our previous work in this area, see
https://www.kth.se/profile/tony/page/deep-networks
Within the scope of this PhD student position, you will work on and contribute to the research frontier regarding scale-covariant or scale-equivariant deep networks and/or deep networks parameterised in terms of Gaussian derivatives, on specific research topics that we choose together within the scope of the research project ”Covariant and invariant deep networks” that finances this position. The overall goal is to develop new architectures for deep networks that can generalise to scaling variations that are not spanned by the training data, and which can achieve higher robustness to variabilities in test data, as well as enable more efficient training with lower requirements concerning the amount of training data.
Third-cycle subject: Computer Science
Supervision: Prof. Tony Lindeberg
What we offer
- The possibility to study in a dynamic and international research environment in collaboration with industries and prominent universities from all over the world.Read more
- A workplace with many employee benefits and monthly salary according to KTH’s Doctoral student salary agreement.
- A postgraduate education at an institution that is active and supportive in matters pertaining to working conditions, gender equality and diversity as well as study environment.
- Work and study in Stockholm, close to nature and the water.
- Help to relocate and be settled in Sweden and at KTH.
Admission requirements
To be admitted to postgraduate education (Chapter 7, 39 § Swedish Higher Education Ordinance), the applicant must have basic eligibility in accordance with either of the following:
- passed a second cycle degree (for example a master’s degree), or
- completed course requirements of at least 240 higher education credits, of which at least 60 second-cycle higher education credits, or
- acquired, in some other way within or outside the country, substantially equivalent knowledge
- to be admitted to the third-cycle education in Computer Science, the applicant must have passed courses resulting in at least 60 credits at minimum second-cycle level in Computer Science or other subjects deemed directly relevant to the chosen specialization.
In addition to the above, there is also a mandatory requirement for English equivalent to English B/6, read more here
Selection
In order to succeed as a doctoral student at KTH you need to be goal oriented and persevering in your work. Candidates will be assessed upon their ability to:
- independently pursue his or her work
- collaborate with others,
- have a professional approach and
- analyse and work with complex issues and
The candidate should have very good knowledge in mathematics (analysis and linear systems, which we use for modelling convolution transformations and geometric image transformations) as well as in structured programming to write code that is easy to use for making experiments with, maintain and develop and share with colleagues. You must have very good knowledge about programming deep networks in Python, PyTorch is meritorious.
Knowledge in computer vision and image analysis is strongly meritorious.
After the qualification requirements, great emphasis will be placed on personal competency.
Target degree: Doctoral degree
Information regarding admission and employment
Only those admitted to postgraduate education may be employed as a doctoral student. The total length of employment may not be longer than what corresponds to full-time doctoral education in four years ‘ time. An employed doctoral student can, to a limited extent (maximum 20%), perform certain tasks within their role, e.g. training and administration. A new position as a doctoral student is for a maximum of one year, and then the employment may be renewed for a maximum of two years at a time.
Union representatives
Contact information KTH’s website.
Doctoral section (Students’ union on KTH Royal Institute of Technology)
Contact information section’s website.
Application
Apply for the position and admission through KTH’s recruitment system. It is the applicant’s responsibility to ensure that the application is complete in accordance with the instructions in the advertisement.
Applications must be received at the last closing date at midnight, CET/CEST (Central European Time/Central European Summer Time).
Applications must include:
- CV including your relevant professional experience and knowledge.
- Application letter with a brief description of why you want to pursue research studies, about what your academic interests are and how they relate to your previous studies and future goals. (Maximum 2 pages long)
- Copies of diplomas and grades from previous university studies and certificates of fulfilled language requirements (see above). Translations into English or Swedish if the original document is not issued in one of these languages. Copies of originals must be certified.
- Representative publications or technical reports. For longer documents, please provide a summary (abstract) and a web link to the full text.
Other information
Striving towards gender equality, diversity and equal conditions is both a question of quality for KTH and a given part of our values.
For information about processing of personal data in the recruitment process please read here.
We firmly decline all contact with staffing, recruitment agencies and job ad salespersons.
Disclaimer: In case of discrepancy between the Swedish original and the English translation of the job announcement, the Swedish version takes precedence.
Location : Sweden
Closing Date : 31st January 2023
HOW TO APPLY FOR THE SCHOLARSHIP
Click Here to Submit your Application on the University Website
PhD in Spatio-temporal models for large citizen science data sets at University of Kent
PROJECT TITLE:
Spatio-temporal models for large citizen science data sets
Supervisors: This is a joint project between the University of Kent and Butterfly Conservation and the PhD student will be supervised by a team with expertise in Statistics, Statistical Ecology, Citizen Science, and Butterfly Monitoring
University of Kent: Dr Eleni Matechou, Dr Diana Cole, Prof Byron Morgan
Butterfly Conservation: Dr Emily Dennis, Dr Richard Fox
Scientific background
At a time of biodiversity loss, including widely reported insect declines, citizen science data play a vital role in measuring changes in species’ populations and distributions and in seeking to understand the pressures influencing such changes.
Butterflies and moths (Lepidoptera) respond quickly to habitat and climatic change, and hence are valuable biodiversity indicators. In the UK, millions of species occurrence records for Lepidoptera have been gathered by two large citizen science recording schemes, of which the full potential has not been fully realized.
Analysing recording data of this nature presents unique challenges relating to their vast quantity but also associated sampling biases. Using cutting edge modelling, this project will maximise these valuable datasets to enhance our understanding of species’ phenology (flight periods), distribution and range dynamics to help inform future conservation delivery and policy for UK butterflies and moths.
Research methodology
The student will undertake new statistical model developments applied to citizen science data. The research will involve:
- Critically assessing sampling design to determine how much data are needed to reliably estimate species’ occurrence trends – can occupancy models be used for rare species with small ranges?
- Modelling species’ phenology from citizen science data to provide new insights on variation over space and time.
- Applying state-of-the-art variable selection techniques to better describe drivers of species’ range and distribution change through suitable spatial and environmental covariates.
Training
The student will develop a strong, highly transferable skillset in statistical modelling and analysis using modern statistical and computational techniques applied to large, unstructured data sets spanning multiple species, locations and years. The student will benefit from interactions with conservation professionals at Butterfly Conservation, including opportunities to undertake fieldwork, to better understand the data collection processes and focal taxa of the project, as well as data use for conservation delivery and policy.
Research excellence
The student will join the thriving Statistical Ecology @ Kent research group, being supervised by leading researchers in statistics and statistical ecology. They will also be members of the UK-wide National Centre for Statistical Ecology. They will attend London Taught Course Centre training, NCSE seminars, and SE@K specialist training and they will present research results at a range of appropriate national and international conferences. There will be ample opportunity for independent development, with the student gaining transferable knowledge of modern data science and statistics.
Please email Dr Eleni Matechou ([email protected]) if you are interested in applying for the project or have any questions about the project or the application process.
Application Process
Deadline for applications: midnight 20th of February 2023
Applicants should follow the University of Kent’s online application process.
Please create an account and add your personal details as requested. Subsequently, you need to select your starting date (September 2023) and write your personal statement (see below). Choose “Other” for source of funding and “Definite” for funding. Add details of your qualifications, and then in the Research Information Tab, write “Dr Eleni Matechou” under supervisor and the title of the project (“Spatio-temporal models for large citizen science data sets“) as the research topic. You do not need to add a research proposal.
As part of the process, you need to provide the following:
o details of your qualifications;
o two academic references;
o a personal statement
The statement must be maximum 500 words detailing (1) your reason for applying for a doctoral studentship (i.e, why do you want to pursue doctoral studies) and (2) your fit with the proposed project (how your educational/professional/personal background has prepared you well to undertake research in this topic).
Person specification
We seek a candidate with a strong quantitative background, for example an MSc in Statistics or an MSc with high statistics content, or a background in ecological modelling. Experience coding in R, or similar, is essential. An interest in conservation and ecology is advantageous. Quantitative ecologists are encouraged to apply.
The University of Kent requires all non-native speakers of English to reach a minimum standard of proficiency in written and spoken English before beginning a postgraduate degree. For more information on English language requirements, please visit this page.
Scholarship Information
VC scholars will receive the following:
- Annual stipend at UKRI rates (£17,668 in 2022/23);
- Annual tuition fees at Home rates (£4,596 in 2022/23)
o 2023/24 rates to be announced.
Selection Process
Supervisors will carry out the initial eligibility checks and shortlisting. Shortlisted candidates will be notified by the 27 of February and will be invited for an interview taking place the on the 7th of March.
Deadline
The deadline for applications is midnight on 20th of February.
Relevant literature
Diana, A., Dennis, E. B., Matechou, E., Morgan, B. J. T. (2022) Fast Bayesian inference for large occupancy datasets. Biometrics, . ISSN 0006-341X. (In press) (KAR id:98286)
Dennis, E.B., Morgan, B.J.T., Freeman, S.N., Ridout, M.S., Brereton, T.M., Fox, R., Powney, G.D., Roy, D.B. (2017) Efficient occupancy model-fitting for extensive citizen-science data. PLoS ONE 12(3): e0174433. https://doi.org/10.1371/journal.pone.0174433
, , , , & (2022) A general modeling framework for open wildlife populations based on the Polya tree prior. Biometrics, 00, 1– 13. https://doi.org/10.1111/biom.13756
Griffin, J. E., Matechou, E., Buxton, A. S., Bormpoudakis, D., & Griffiths, R. A. (2020). Modelling environmental DNA data; Bayesian variable selection accounting for false positive and false negative errors. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69(2), 377-392.
Dennis, E.B., Morgan, B,J.T, Freeman, S.N., Brereton, T.M. & Roy, D.B. (2016). A generalized abundance index for seasonal invertebrates. Biometrics, 71, 1305-1314.
Dennis, E.B., Brereton, T.M., Morgan, B.J.T., Fox, R., Shortall, C.R., Prescott, T. & Foster, S. (2019). Trends and indicators for quantifying moth abundance and occupancy in Scotland. Journal of Insect Conservation, 23, 369-380.
Location : Kent
Closing Date : 20th of February 2023
HOW TO APPLY FOR THE SCHOLARSHIP
Click Here to Submit your Application on the University Website
PhD in Natural Language Processing at Queen Mary University London
Level: PhD
Country: Please see eligibility criteria below
Value: Tuition fees and a London stipend of £19,668 per year
No. of awards: 9
Deadline: January 31st 2023
About the Studentships
The school of Electronic Engineering and Computer Science of the Queen Mary University of London is inviting applications for up to 9 PhD Studentships in specific areas in Electronic Engineering and Computer Science (please see the list of projects at the end of this page). The PhD studentships will cover tuition fees and offer a London stipend of £19,668 per year. The scholarships are open to both home and international candidates (please see below the eligibility criteria and the details on the tuition fees depending on the applicant status).
About the School of Electronic Engineering and Computer Science at Queen Mary
The PhD Studentship will be based in the School of Electronic Engineering and Computer Science (EECS) at Queen Mary University of London. As a multidisciplinary School, we are well known for our pioneering research and pride ourselves on our world-class projects. We are 8th in the UK for computer science research (REF 2021) and 7th in the UK for engineering research (REF 2021). The School is a dynamic community of approximately 350 PhD students and 80 research assistants working on research centred around a number of research groups in several areas, including Antennas and Electromagnetics, Computing and Data Science, Communication Systems, Computer Vision, Cognitive Science, Digital Music, Games and AI, Multimedia and Vision, Networks, Risk and Information Management, Robotics and Theory
For further information about research in the school of Electronic Engineering and Computer Science, please visit: http://eecs.qmul.ac.uk/research/.
Who can apply
Queen Mary is on the lookout for the best and brightest students. A typical successful candidate:
- Should hold, or is expected to obtain an MSc in the Electronic Engineering, Computer Science, or a closely related discipline
- Having obtained distinction or first class level degree is highly desirable
Eligibility criteria and details of the different schemes
EPSRC-DTP:
- 3.5 years stipend and fees
- Details: Open to home and international students. Please note that the number of students with International fee status which can be recruited is capped according to the EPSRC terms and conditions so competition for International places is particularly strong.
- Expected start date: September 2023
Principal Scholarships:
- 3 years stipend and fees
- Details: Open to home students. Please note that the scheme covers stipend and home tuition fees – for candidates with international fee status the difference needs to be covered from other sources.
- Expected start date: September 2023
How to apply
Queen Mary is interested in developing the next generation of outstanding researchers and decided to invest in specific research areas. For further information about potential PhD projects and supervisors please see the list of the projects at the end of this page.
Applicants should work with their prospective supervisor and submit their application following the instructions at: http://eecs.qmul.ac.uk/phd/how-to-apply/
The application should include the following:
- CV (max 2 pages)
- Cover letter (max 4,500 characters) stating clearly in the first page whether you are eligible for a scholarship as a UK resident (see the link below)
- Research proposal (max 500 words)
- 2 References
- Certificate of English Language (for students whose first language is not English)
- Other Certificates
Please note that in order to qualify as a home student for the purpose of the scholarships, a student must have no restrictions on how long they can stay in the UK and have been ordinarily resident in the UK for at least 3 years prior to the start of the studentship. For more information please see: https://www.ukri.org/what-we-offer/developing-people-and-skills/esrc/funding-for-postgraduate-training-and-development/eligibility-for-studentship-funding/
Application Deadline
The deadline for applications is the 31st of January 2023.
For general enquiries contact Mrs. Melissa Yeo [email protected] (administrative enquiries) or Professor Ioannis Patras [email protected] (academic enquiries) with the subject “EECS 2023 PhD scholarships enquiry”.
Location : London
Closing Date : 31st of January 2023
HOW TO APPLY FOR THE SCHOLARSHIP
Click Here to Submit your Application on the University Website
PhD in Improving project cost estimates using Artificial Intelligence and Machine Learning
Introduction
An exciting opportunity to apply for eight fully funded PhD positions in the College of Arts, Technology and Environment, UWE Bristol.
Ref: 2223-APR-CATE15
The expected start date of these studentships is 1 April 2023.
The closing date for applications is 8 January 2023.
Please note: out of the eight projects being advertised for the CATE Studentships 2022. The projects for funding will be selected based on the merit of applicants following the process outlined below.
Studentship details
Construction projects notoriously suffer from poor cost prediction and cost-overrun. Poor cost performance remains a challenge for the construction industry worldwide. Despite recent methodological and technological advances in the field, project-cost overrun remains a significant challenge for the industry. Government has emphasised the need for the industry to transform, from a management-focused change agenda to one that is more technocentric that views digital technologies and other Industry 4.0-enabling technologies as initiatives to enhance the industry.
This doctoral study will propose practical digital solutions that can overcome the challenges and promote the opportunities to improve cost performance of construction projects using Artificial Intelligence (AI) and Machine Learning (ML). Use of AI has helped to achieve significant enhancement of service processes and industry productivity in recent years, alongside enhance automation, and provides competitive advantage as compared to conventional approaches. This research aims to develop a novel approach which will adopt AI in the interpretation and analysis of cost data that would generate more accurate and reliable order of cost estimates (OCE) and related cost risk assessment for construction projects. It will use AI and Machine Learning techniques to mine historic data of disparate quality and formats to enable its analysis and, to extend the techniques to produce more accurate cost predictions. This approach also has the potential to deliver improved cost management by shining light on the drivers of high costs and providing the evidence needed for improved cost prediction, analysis and reduction.
This project will lie at the interface between built environment and data science/AI, and motivated applicants from disciplines related to either field are welcomed but must be able to work well as a part of a multi-disciplinary team. The scope of the project can be tailored as required and the successful applicant will be supported by a multi-disciplinary team of supervisors.
The successful candidate will be a member of the Centre for Architecture and Built Environment Research Centre for Architecture and Built Environment Research (CABER). CABER seeks to develop innovative approaches, procedures, technologies and techniques that support the design, management, reconfiguration, maintenance and operation of buildings, their fabric and the environments they create. In the recent Research Assessment Exercise 88% of research in ABE was judged to be Internationally Excellent or World Leading.
For an informal discussion about the studentship, please email Dr Ndibarafinia Young Tobin at [email protected].
You can also contact Professor Jessica Lamond at [email protected] or Dr Stephen Hall at [email protected] about the studentship programme.
Funding
The studentship is available from 01 April 2023 for a period of three and half years, subject to satisfactory progress and includes a tax exempt stipend, which is currently £17,668 per annum.
In addition, full-time tuition fees will be covered for up to three years.
Eligibility
Applicants must have a Bachelor’s degree in quantity surveying/ construction project management/ construction management/ or related construction degree.
Ideally the candidate will have some of the following:
- a Master’s degree or equivalent research experience
- experience in /familiarity with the UK’s construction industry
- a background in workplace design/management
- knowledge of qualitative research methods, research integrity and ethics
- Ability to learn new fields of application of data science.
Relevant experience:
- AI/Machine Learning tools and techniques
- Experience in AI and cost estimating.
A recognised English language qualification is required.
How to apply
Please submit your application online. When prompted use the reference number 2223-APR-CATE15.
Supporting documentation: You will need to upload your research proposal, all your degree certificates and transcripts and your proof of English language proficiency as attachments to your application, so please have these available when you complete the application form.
References: You will need to provide details of two referees as part of your application. At least one referee must be an academic referee from the institution that conferred your highest degree. Your referee will be asked for a reference at the time you submit your application, so please ensure that your nominated referees are willing and able to provide references within 14 days of your application being submitted.
Location : Bristol
Closing Date : 08 January 2023.
Click Here to Submit your Application on the University Website
Further Information
It is expected that interviews will take place on weeks commencing 20 February 2023. If you have not heard from us by February, we thank you for your application but on this occasion you have not been successful
PhD in Computer vision and artificial intelligence at Edge Hill University
Graduate School
Salary: Each GTA studentship includes a ‘package’ with a total value just under £26,000 per annum for UK based applicants and over £34,000 for international applicants per annum. Commencing Saturday 30 September 2023.
Post Type: Full Time
Reference: GTA18-1122
A fantastic opportunity to study for a 3-year funded doctorate whilst gaining valuable teaching experience.
About the Research Project
All advertised projects for this research area can be found here.
Please direct any questions about the project to Professor Ardhendu Behera ([email protected]) in advance of submitting your application.
To apply for this research area, please scroll to the bottom of the page and click the ‘apply’ button. Contact [email protected] if you have difficulty accessing the form.
About You
You will have a 2.1 or above undergraduate degree in a relevant subject, whilst a Masters degree would also be an advantage. You should have a passion for pursuing original research in your subject area along with the desire to develop your teaching ability by supporting an experienced and dynamic undergraduate programme team.
Highly motivated, resilient and with the determination to succeed, you will be committed to undertaking a PhD and will possess strong written and oral communication skills along with excellent organisational skills.
Please note:
- If you already have a PhD in the research area you are applying for, your application will not normally be considered unless submitted for a suitably different research area.
- Applicants who have applied more than two times previously should not normally apply again with the same research project.
About the Graduate Teaching Assistant Role
Graduate Teaching Assistants hold a unique position in the University, being both registered postgraduate researchers (PGRs) and carrying out teaching/teaching support duties.
PGRs will be expected to:
- Complete a programme of researcher development sessions.
- Undertake tailored continuing researcher development.
- Complete a programme of training in teaching in Higher Education.
- Complete a PhD programme of research under the guidance of an appointed supervisory team.
- Undertake up to six hours teaching a week as directed by the Head of Department.
- Undertake up to nine hours per week of marking and preparation.
- Enhance the research culture of the department in which they are located, and of the University as a whole, by participating in development activities including publication and presenting at conferences.
Rewards & Benefits
We want you to feel happy when you come to work and proud when you go home. From the moment you join us you have the opportunity to enhance your skills. In addition to having the opportunity to enrol on our Postgraduate Certificate in Teaching in Higher Education (PGCTHE) to support your professional development, we offer a range of specialist development sessions and courses. In addition to the wide range of development opportunities available, the University has a strong focus on the wellbeing of its staff. You will have access to an award-winning staff health & wellbeing programme, benefit from discounted membership to our state-of-the-art sport and leisure facilities and so much more. This is just a taste of what we can offer you at Edge Hill University.
Each GTA studentship includes a ‘package’ with a total value just under £26,000 per annum for UK based applicants and over £34,000 for international applicants per annum. This includes:
- Overall payment of £17,668 per annum which is aligned with the UKRI rate. This is split into a salary of £12,168 per annum and £5,500 per annum tax free to contribute to the cost of accommodation
- Full waiver of research degree tuition fees worth approximately £4,700 per annum for UK PGRs and £14,000 per annum for international PGRs
- Full waiver of the Postgraduate Certificate in Teaching in Higher Education tuition fee (worth approximately £1740)
- Entitlement to sick pay, maternity, paternity, and shared parental leave, in accordance with University policies and procedures.
Applicant Guidance
- Applicant guidance: Applicant guidance can be found here. Please read this before applying.
- Project contact: Staff contacts for each research area can be found on the webpage lined under ‘about the research project’ above. You are strongly encouraged to speak with them about the area you are interested in, in order to identify a prospective supervisor in advance of applying.
- Job description and person specification: You should read the job description and person specification carefully.
How to Apply
At Edge Hill University we value the benefits a rich and diverse workforce brings to our community and therefore welcome applications from all sections of society.
Applicants must complete the online application form via www.edgehill.ac.uk/jobs and attach 5 separate documents in the following order:
- A ‘Research Proposal’ which should not exceed 2,000 words
- A full academic curriculum vitae
- Qualification certificates – please ensure these are translated into English
- Scanned copy of your passport
- IELTs, or equivalent, paperwork – click here for detailed guidance (international and EU applications only).
- It is very important you review the guidance and seek clarification regarding this paperwork before applying if you have any questions.
- Letters of recommendation, or requests to waive the language entry requirements, will not be accepted.
Applications without the above paperwork will not be considered for shortlisting.
To apply for this research area, please scroll to the bottom of the page and click the ‘apply’ button. Contact [email protected] if you have difficulty accessing the form.
Interview Arrangements
Interviews are expected to be held in March 2023. The exact date will be confirmed in due course. Panel availability is limited. Shortlisted candidates will be offered one interview slot only.
Project Information is available alongside the job description above.
Key Contacts
Please think about the support you need and contact one of the areas directly.
- Research project and supervision questions: see the details under ‘about the research project’ to speak with the research degree contact responsible for your area of interest
- Application form and employment questions: please contact [email protected]
- PhD registration and qualification questions: please contact [email protected]
Staff availability
Please note that the university is closed from the evening of the 22nd December 2022 until 3rd January 2023. Staff will respond to you upon their return. It is highly advised that you speak with staff at the earliest opportunity to avoid encountering any delays due to the scheduled break.
Location : England
Closing Date : 09 January 2023
Click Here to Submit your Application on the University Website
PhD in AI-based Crop Disease Monitoring and Detection at Ulster University
Summary
Plant diseases may affect the root, steam and leaves of plants resulting in a sizable drop of revenue for farmers as crop’s quality is affected and may lead to food shortage and food chain disruption [1]. Traditionally, a crop disease can be detected by visual inspection which can be a tedious enterprise which is time and effort consuming, and errors prone. Farming has developed extensively in the last few decades taking advantages from developments in chemistry, physics, sensing technology, data processing and analytics, artificial intelligence and IoT [1,3-4]. The demand for mobile portable applications in agriculture has increased as portable technology ubiquitousness allows for a wider deployment and a better cost-effectiveness. With the technology, farmers can identify and detect early infections and diseases and hence mitigate their impact, improve treatments outcome and can prevent further infections from re-occurring. Portable spectroscopy can be used to detect the presence of diseases on leaves and categorise healthy plants from unhealthy ones. Such a technology has found use in many agro-food applications as it offers short processing times, cost-effectiveness, portability and ease-of-deployment [2,5].
Spectroscopy is the analysis of matter and its interaction with electromagnetic radiations; and a spectral signature is the variation of reflectance or emittance of a material with respect to wavelengths. It is a non-destructive way to find the fingerprints of components; and hence is a suitable method to inspect plants’ samples.
Reflectance is a measure of electromagnetic energy that bounces back from the surface of a material; and the leaf reflectance in the visible and near-infrared ranges are influenced by a variety of interactions (including leaf surface and water content) which can lead to a suitable use in classification and detection. Further, green vegetation spectral signatures can show pigmentation in plant tissues as Chlorophyll growth is affected. Hence it can be used for anomaly detection in remote sensing applications. Counting the number of insects of various species is important for planning pest control, and for guiding agricultural policy. Computer vision algorithms can be trained with the captured footage to detect the soil conditions, analyse the aerial view of the overall agricultural land, and assess crop health information. Computer vision-enabled machines can be used in sorting and grading the harvest; while automating such tasks can offer efficiency [2,3].
Hyperspectral imaging in agriculture can significantly extend the range of farming issues that can be addressed using remote sensing. Almost every farming issue (weeds, diseases, etc.) changes the physiology of plants, and therefore affects its reflective properties. Healthy and unhealthy crops reflect the sun light differently which renders it possible to detect such changes in the physiology of the plants and correlate them with spectra of reflected light.
Hence the objectives of this research proposal are:
To address the complexity of crop disease monitoring and detection in the context of smart farming taking account of different data types.
To develop a solution that integrates both computer vision and spectroscopy related information.
To design an AI based system for classification of diseases and anomaly detections.
Essential criteria
Applicants should hold, or expect to obtain, a First or Upper Second Class Honours Degree in a subject relevant to the proposed area of study.
We may also consider applications from those who hold equivalent qualifications, for example, a Lower Second Class Honours Degree plus a Master’s Degree with Distinction.
In exceptional circumstances, the University may consider a portfolio of evidence from applicants who have appropriate professional experience which is equivalent to the learning outcomes of an Honours degree in lieu of academic qualifications.
Desirable Criteria
If the University receives a large number of applicants for the project, the following desirable criteria may be applied to shortlist applicants for interview.
- First Class Honours (1st) Degree
- Masters at 70%
- Experience using research methods or other approaches relevant to the subject domain
- Work experience relevant to the proposed project
- Publications – peer-reviewed
Funding and eligibility
The University offers the following levels of support:
Vice Chancellors Research Studentship (VCRS)
Full award (full-time PhD fees + DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £18,000 (tbc) maintenance grant per annum for three years (subject to satisfactory academic performance).
This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Vice-Chancellor’s Research Bursary (VCRB)
Part award (full-time PhD fees + 50% DfE level of maintenance grant + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees and provide the recipient with £8,000 maintenance grant per annum for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Vice-Chancellor’s Research Fees Bursary (VCRFB)
Fees only award (PhD fees + RTSG for 3 years).
This scholarship will cover full-time PhD tuition fees for three years (subject to satisfactory academic performance). This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Department for the Economy (DFE)
The scholarship will cover tuition fees at the Home rate and a maintenance allowance of £18,000 (tbc) per annum for three years (subject to satisfactory academic performance).
This scholarship also comes with £900 per annum for three years as a research training support grant (RTSG) allocation to help support the PhD researcher.
- Candidates with pre-settled or settled status under the EU Settlement Scheme, who also satisfy a three year residency requirement in the UK prior to the start of the course for which a Studentship is held MAY receive a Studentship covering fees and maintenance.
- Republic of Ireland (ROI) nationals who satisfy three years’ residency in the UK prior to the start of the course MAY receive a Studentship covering fees and maintenance (ROI nationals don’t need to have pre-settled or settled status under the EU Settlement Scheme to qualify).
- Other non-ROI EU applicants are ‘International’ are not eligible for this source of funding.
- Applicants who already hold a doctoral degree or who have been registered on a programme of research leading to the award of a doctoral degree on a full-time basis for more than one year (or part-time equivalent) are NOT eligible to apply for an award.
Due consideration should be given to financing your studies. Further information on cost of living
Recommended reading
N.N. Che’Ya,, N.A. Mohidem, ; Roslin, N.A.; Saberioon, M.; Tarmidi, M.Z.; Arif Shah, J.; Fazlil Ilahi, W.F.; Man, N. Mobile Computing for Pest and Disease Management Using Spectral Signature Analysis: A Review. Agronomy 2022, 12, 967.
F. Asharindavida, O. Nibouche, J. Uhomoibhi, H. Wang, and J. Vincent, “Evaluation of olive oil quality using a miniature spectrometer: A machine learning approach,” in Proc. SPIE, vol. 11754, pp. 17–28, Apr. 2021.
P. A. Dias, A. Tabb, and H. Medeiros, “Multispecies Fruit Flower Detection Using a Refined Semantic Segmentation Network,” IEEE Robot. Autom. Lett., vol. 3, no. 4, pp. 3003–3010, 2018.
Daniel Caballero, Rosalba Calvini, José Manuel Amigo,Hyperspectral imaging in crop fields: precision agriculture, Data Handling in Science and Technology, Elsevier, vol.32,2019, pp.453-473.
M. Ahmad, Muhammad, Asad Khan, Adil Mehmood Khan, Manuel Mazzara, Salvatore Distefano, Ahmed Sohaib, and O. Nibouche. 2019. “Spatial Prior Fuzziness Pool-Based Interactive Classification of Hyperspectral Images” Remote Sensing 11, no. 9: 1136
Location : Northern Ireland
Closing Date : 27 FEBRUARY 2023
HOW TO APPLY FOR THE SCHOLARSHIP
Click Here to Submit your Application on the University Website