Posts Tagged “Scholarships in UK”
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 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
PhD in Artificial Intelligence and Music at Queen Mary University London
UKRI Centre for Doctoral Training in Artificial Intelligence and Music (AIM)
12+ Fully-funded PhD studentships to start September 2023
Call open to UK Home and International student applicants
Covers fees and a stipend for four years
Application deadline: 31 January 2023
Why apply to the AIM Programme?
- 4-year fully-funded PhD studentships available
- Extensive choice of projects, drawing on a supervisory team of over 30 academics
- Access to cutting-edge facilities and expertise in artificial intelligence (AI) and music/audio technology
- Comprehensive technical training at the intersection of AI and music through a personalized programme
- Partnerships with over 25 companies and cultural institutions in the music, audio and creative sectors
For more information on the AIM Programme structure click here.
Who should apply?
We are on the lookout for outstanding students interested in the intersection of music/audio technology and AI. Successful applicants will have the following profile:
- Hold or be completing a Masters degree at distinction or first class level, or equivalent, in Computer Science, Electronic Engineering, Music/Audio Technology, Physics, Mathematics, or Psychology. We also accept applicants holding or completing a Masters degree at distinction or first class level, or equivalent, in Music Performance/Composition or Musicology, provided that the applicant has a suitable technical background to undertake a PhD in AI and Music. In exceptional circumstances we accept applicants with a first class Bachelors degree who do not hold a Masters degree, provided that applicants can provide evidence of equivalent research experience, industry experience, or specialist training.
- Programming skills are strongly desirable; however we do not consider this to be an essential criterion if candidates have complementary strengths.
- Musical training (any of performance, production, composition or theory) is desirable but not a prerequisite.
For this call we are accepting applications from UK Home students and International students, as well as students supported by national and international funding bodies, such as the China Scholarship Council (CSC), CONACYT, and the Commonwealth PhD Scholarships scheme. Queen Mary’s commitment to our diverse and inclusive community is embedded in our student admissions processes. We particularly welcome applications from women and under-represented groups, and from applicants in all stages of life.
Funding
We have 12+ fully-funded 4-year PhD studentships available for students starting in September 2023 which will cover the cost of tuition fees and will provide an annual tax-free stipend (£19,668 in 2022/23). The CDT will also provide funding for conference travel, equipment, and for attending other CDT-related events. For more information on external PhD studentships and self-funded please visit http://www.aim.qmul.ac.uk/apply .
External PhD studentships
The AIM programme welcomes applications from students who have sponsorship for PhD study from numerous international funding agencies including:
China Scholarship Council (CSC)
Commonwealth PhD Scholarships
CONACYT (Mexico)
Higher Education Commission, Pakistan
Islamic Development Bank
Please see the PhD funding page for full details of Queen Mary’s funding partners, including other schemes not listed here.
Important information for CSC applicants: the AIM programme is able to offer tuition fee waivers to applicants who receive PhD studentships from the China Scholarship Council (CSC). Applications must state on their application form that they intend to apply for CSC funding. Following the PhD offer acceptance, applicants will then need to submit a separate studentship application to the CSC. Following a change in CSC regulations, applicants should ensure that they have met the English language requirements by the point of offer in order to be eligible for CSC funding.
Self-Funding
It is always possible to apply to the AIM programme as a self-funded student or with funding from another source. Please be aware that you will need to be able to prove that you have the funding to cover the tuition fees, living costs, and the additional travel and accommodation costs required for this course.
Tuition fees vary according to your fee status. To discuss living costs, tuition fees and additional programme costs, please contact us at [email protected]
Visa fees and healthcare surcharge
Please note that for international students on a student visa, the AIM programme is not able to cover any visa application fees and/or the healthcare surcharge (currently at £400 per year).
How to apply
To apply for the AIM PhD Programme, complete the online application form which can be found at:
https://mysis.qmul.ac.uk/urd/sits.urd/run/siw_ipp_lgn.login?process=siw_ipp_app&code1=RFQM-G4ZG-09&code2=0005
The deadline for applications is on 31 January 2023 (23:59 GMT). Interviews are expected to take place in early March. Decision notifications will be sent in April and May.
What you should include in your application:
- Fully complete application form, transcripts of your previous degree(s), and a Curriculum Vitae PDF document. Overseas applicants from non-English speaking countries must include a proof of English language ability (see details on English requirements below).
- A two page A4 “Research Proposal” – a PDF document in which you provide a description of your top choice of PhD topic. You can either: (i) Select a PhD topic from the list of suggested AIM PhD topics; or (ii) Propose your own PhD topic, which will have to fall in the intersection of Artificial Intelligence and Music. In your research proposal, we strongly encourage you to list up to 3 PhD topics in order of preference, either from the list of suggested AIM topics or based on your own suggested topic(s). You should describe only one topic in detail in your research proposal document. You should use 11pt fonts in the document; references do not count in the page limits. We encourage you to contact your chosen supervisor for an informal discussion – this will also help you to put together your research proposal, which is an integral part of your application. Note: submission of a research proposal is mandatory, irrespectively if you are choosing a topic from the pool of suggested PhD topics or if you are proposing your own topic; applications without a research proposal will not be considered.
- A one page A4 “Personal Statement” – a PDF document in which you set out your previous academic or other experience relevant to your proposed research interests, why you wish to undertake this research at QMUL, and why you want to do the AIM PhD – think of it like a covering letter to explain why you want to apply for the AIM PhD.
- Two references are required – either two academic, or one academic and one work related reference. Your academic referee must be able to comment on the standard of your academic work and suitability for postgraduate level study. Where appropriate, a second referee can provide comment on your professional experience (if you are unable to obtain references prior to submission please send/ask your referee to send it to the following email address, including the application number in the subject: [email protected]).
- Select a supervisor – in case you have chosen a PhD topic from the list of suggested AIM PhD topics, this would be the topic’s respective supervisor. In case you are suggesting your own topic, have a look at the list of academics at the School of EECS: http://eecs.qmul.ac.uk/people/academic/ and the research groups in the School: http://eecs.qmul.ac.uk/research/research-groups/. Make sure to contact possible supervisors in advance and agree who would be you supervisor and what the topic would be.
- In the box “How do you intend to finance your studies at Queen Mary” – if you are not looking for a scholarship and you will be self-funded please state “self-funded”. If you have or are applying for another source of funding such as China Scholarship Council please state this.
Additional tips for completing your application:
- The online application link above will take you directly to the “PhD FT Artificial Intelligence and Music – Semester 1 start” application form (Course reference RFQM-G4ZG-09). Please use that link to complete your application.
- Note that each new PhD cohort starts in September, you cannot start at any other time of the year.
- For faster processing of your application, please submit all application documents in PDF format (please avoid uploading DOC, DOCX, JPG, PNG etc files).
- Please note: We will share the application details you submit to Queen Mary with the AIM programme industry/academic partners (when the proposed or selected project you have applied for involves an external partner) since it will be necessary for admission selection and the performance of any contract between you and Queen Mary/AIM CDT. Please refer to the privacy notice for applicants and our Data Protection Policy for further information.
- If you are an overseas student there is helpful advice on our International pages and on the UK Council for International Student Affairs website.
- The standard requirement for English at the School of EECS is IELTS 6.5 overall or an equivalent certificate. More details about language requirements for postgraduate research students in the School of Electronic Engineering and Computer Science can be found in the international students section.
- If you have a serious problem with the online application form please email [email protected]. For informal enquiries related to the AIM Programme and application process please email any questions to [email protected]
- We strongly encourage you to list in your application more than one PhD topic, preferably 3 topics (this information should be stated in the research proposal document).
- In the online application form, under section “Other Information”: for question “How do you intend to finance your studies at Queen Mary University of London?” please use the “I am a QMUL PG Research Studentship applicant” response.
- The AIM PhD Programme is a four year full-time programme and you must be able to commit full-time to the programme. In exceptional circumstances the programme allows for part-time study at 50% minimum; if you wish to be considered for part-time study, please include your justification for part-time study in a cover letter submitted with your application.
- We recommend applicants to check these 3 resources:
- FindaPhD – Writing a Good PhD Research Proposal: https://www.findaphd.com/advice/finding/writing-phd-research-proposal.aspx
- QMUL DCE CDT website – it has presentation slides and video recordings of some workshops run by the CDT with guidelines on writing your research proposal: https://www.qmul.ac.uk/dce/applications/
- IGGI CDT website – on recommendations for writing a research proposal.
Location : London
Closing Date : 31 January 2023
Click Here to Submit your Application on the University Website
PhD in Exploring Decision-making Around Data Access at University of West England
Introduction
College of Business and Law at the University of the West of England in Bristol invites applications for a fully funded PhD studentship in Data Science on the topic of “Exploring Decision-making Around Data Access”.
Start date of this studentship: 1 April 2023
Studentship details
Greater availability of confidential government data for research has dramatically increased the opportunities for policy-relevant social science analysis. This has been driven by two factors: technological and procedural developments allowing highly secure but researcher-friendly analysis environments, and legislative changes.
However, there are still significant barriers to greater use of public data resources, and these arise partly from the institutional structure of government. These barriers can arise at different stages: at the conceptual level of granting access to data sources, when determining rules for using the data, when reviewing specific applications.
This PhD will investigate these institutional barriers. The PhD proposal may seek to explore the topic from an economic, psychological, organisational or other perspective, but should include a strategic concept of primary research using interviews with relevant parties. The PhD candidate will be able to draw on DRAGoN’s extensive network of contacts, but will also be expected to develop their own sources of information.
The researcher experience is central to an understanding of data access processes. We therefore expect that the PhD candidate will either have experience of using confidential data facilities such as the Secure Research Service, Secure Data Service, eDRIS or SAIL, or would be willing to work with UWE microdata researchers to develop that experience.
The studentship reflect ongoing commitment of the College of Business and Law at UWE to the development of its doctoral programmes.
Prospective student will be based in the Bristol Business School, which is home to several multidisciplinary research groups.
Doctoral students at the College benefit from comprehensive support and guidance from UWE Doctoral Academy, including extensive professional development programme, research skills training and careers advice. Find out more about the Doctoral Academy.
For an informal discussion about the studentship, please contact Director of Doctoral Research, Dr Pawel Capik at [email protected].
For an informal discussion about the project proposal please contact Professor Felix Ritchie at [email protected].
Funding
Studentship is available from 1 April 2023 for three years, subject to satisfactory progress, and includes tax exempt stipend at £17,668 per annum.
Full-time home/international tuition fees will be covered for up to three years.
Eligibility
Applicants must have at least 2.1 degree in economics, management or cognate discipline, and preferably Masters degree (with average programme mark of no less than 65%, UK grading scale or international equivalent).
A recognised English language qualification is required. IELTS score of 7.0 overall, or equivalent.
How to apply
- Prepare your research proposal (no more than 5 pages all-inclusive; font size 11, 1.5-spacing, 2-inch margins). This should ideally happen in consultation with the potential supervisor.
- You should contact the potential supervisor with an advanced draft of the proposal to confirm they are willing to support your application.
- Submit your formal application online. When prompted use the reference number 2223-APR-FBL05
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 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, England, UK
Closing Date : 15 January 2023 18:00 pm (UK time)
Click Here to Submit your Application on the University Website
Further Information
Interviews will take place in February. If you have not heard from us by 31 January 2023, we thank you for your application but on this occasion you have not been successful.