For the 2024-2025 intake, there will be two application deadlines:
Please note, projects may be allocated during Round 1, and we strongly encourage you to submit your application as soon as it is ready.
We are looking for highly motivated individuals who are passionate about contributing to new discoveries in drug discovery bioscience through the application of the latest techniques in AI and data science.
We welcome applications from candidates who hold a masters degree at distinction or first class level, or have gained equivalent experience, in Computer Science, Bioinformatics / Computational Biology, Biochemistry, Biotechnology, Biology, Pharmacology, Medicinal Chemistry, Chemistry or other relevant fields. We accept applicants with a first class Bachelors degree who do not hold a Masters degree, provided the applicants provide evidence of equivalent research experience, industry experience, or specialist training. Programming skills are strongly desirable and you will be confident in performing data wrangling and analysis in a language such as Python, R or C++, or can demonstrate the ability to acquire the necessary level of programming skills.
We particularly encourage students from groups that are currently underrepresented in postgraduate science research, including black and minority ethnic students and those from a socio-economically disadvantaged background.
Part-time study is an option for some projects. Please specify this as part of your application if relevant.
We appreciate all talent and skills, and we welcome applicants of all backgrounds and identities. Our applications are open to both home and international students who meet the programme entry criteria. We can allocate up to 30% of our places to International Students; this is in line with the UKRI student eligibility criteria, as detailed in the UKRI Postgraduate funding guide.
To be classed as a home student, candidates must meet the following criteria set out in the UKRI Training Grant Terms & Conditions:
If a candidate does not meet the criteria above, they would be classed as an International student.
Each project description contains details on eligibility which we encourage candidates to review.
If you obtained your qualifications outside of the UK, check country/region specific information on the Queen Mary Webpage.
Applicants whose first language is not English will be required to provide evidence of their English language ability, in line with QMUL admissions policy for postgraduate research programmes. The minimum requirements for the AIDD Doctoral training programme are:
For information on alternative English language qualifications, please see the Queen Mary webpage on alternative qualifications.
The AI for Drug Discovery Programme has two deadlines for applications. You must ensure that you submit your application, and all required attachments prior to the noted deadline. The applications deadlines for the 2024-25 intake are:
Please note, projects may be allocated during Round 1 and we strongly encourage you to submit your application as soon as it is completed. The projects pages will be updated in March 2024 to reflect any new projects, and those no longer available.
You can find projects available for the 2024-2025 intake noted on the projects pages. Please review this and identify up to 2 that you wish to apply for. Candidates can apply for a maximum of 2 projects in any given Round.
Once you have identified a project(s) to apply for, you will need to complete an online application form via the Queen Mary Admissions Portal. The programme code you will need to select is RFQM-I9EE-09 (PhD FT Artificial Intelligence for Drug Discovery).
As part of the application, you will need to attach the following documents. Note, included in the list below is a supplementary application form ("AIDD Application Form 2024-2025") for the programme which you will need to download, complete, and then upload as a single PDF to your application in the Queen Mary Admissions Portal. You can upload it under the "personal statement" option on the Admission portal. Applications that do not include this form will be considered as incomplete.
We have prepared a Guide on how to complete the application form. We recommend you review this in advance of submitting your application.
After submitting your application form, our management team will contact you asking to complete a Diversity Form. This is optional, and will be used for equality and diversity monitoring. It will not form part of your formal application to the programme, will be stored separately, and will not be used as part of the selection process.
Submitted applications will be reviewed by a panel of academics from the AI for Drug Discovery programme, and shortlisted candidates will be invited to attend an interview with the project team of their chosen project. If you have identified 2 projects in your application, you may be invited to attend an interview for both projects.
The selection panel will initially review the material submitted in the AI for Drug Discovery Application form that candidates will complete, and upload as part of their application via the Queen Mary Admissions Portal. This form is designed to be anonymised to support a fair assessment.
Successful candidates will be able to demonstrate:
Applicant's achievements will be considered within the context of their individual circumstances.
We have put together this list of Frequently asked questions based on enquiries received for other doctoral training programmes. If your question isn’t answered here please contact us on email@example.com and one of our management team will get back to you as soon as possible. We will update this list of FAQs with commonly asked queries that we receive.
Is this programme for me?Given the cross-disciplinary nature of the programme, we expect to recruit students from a range of academic backgrounds, from computer science to biochemistry. The specific balance between areas in which each student will be trained will therefore differ significantly as the students develop into interdisciplinary researchers. While this programme is primarily focussed on computer-based research, QMUL will provide with a useful route to experimental validation of novel findings, allowing close integration of experimental biology and data-driven and modelling approaches.
What fees do I have to pay?International and home postgraduate student fees are covered by the studentships in full. We have limited number of places for international PhD students, in line with requirements from our funder.
Can the studentship be undertaken part time?Requests for undertaking PhD research part-time can be considered. Note that this will extend the overall duration of your PhD training (for example from 4 to 8 years if you pursue the programme with 50% time dedication). If you want to pursue your PhD training on a part-time basis, please make sure to indicate this in your personal statement, explaining why you think this is the best option for you.
Is this programme available remotely?This is not a distance learning programme, students are expected to be living in the UK throughout the programme in order to attend some courses, training and industry placements in person.
What documents do I need to submit with my application?
Applications should be submitted via the Queen Mary Admissions Portal and you will need to include the below documents:
How does the industry placement work?Industry placements will last between 3 and 18 months, and all the details will be agreed with your academic and industry supervisors at the beginning of the PhD studies. You will be party to an agreement between Queen Mary, the Industry partner, and yourself which outlines the additional contribution that the Industry partner will make towards your studentship, to facilitate engagement and visits to their offices. This typically is a stipend top-up to cover additional travel, and accommodation during the period of a placement, however this does vary and will be agreed based on the requirements of your specific project, and you may therefore find that it varies between students on the programme.
How does the interview work?If your application is shortlisted, you will be invited to attend an invite with the primary and secondary supervisors on your chosen project. Interviews will either take place in person, or via Zoom, and will typically last up to 30 minutes. You can expect the interview panel to ask you about your motivation to pursue a PhD, your experience to-date, and its fit to the chosen project, as well as why you might wish to undertake the project you have chosen.
How should I select my References?It is important that the individuals you submit as referees can comment on your academic and professional experience, as these will be used in the shortlisting of your application. We recommend that you approach your chosen referees in advance and notify them that you are applying and wish to list them as a reference. Ensure that you discuss the project(s) that you are considering applying for, and that you give them sufficient notice so that they have time to provide a reference by the deadline.
At least one your references should be from an academic setting, ideally someone who can comment on your research abilities. This could be a supervisor on a project, or when conducting lab-work for example. A personal tutor may be a good choice if they are able to comment on your interests and abilities, as well as your personal goals, or a previous employer if they are able to comment on your interests or skills related to your research area.
Do I need to contact the Supervisor of my chosen project before applying?You do not need to contact the supervisor(s) listed on the project you are interested in prior to applying. If you have a question on the project, or are unsure on anything, you can reach out to the supervisor directly or you can contact firstname.lastname@example.org who will be able to provide further support.
Prof Noor Shaker is a serial biotech entrepreneur and the CEO at GlamorousAI, a biotech company that pushes the boundaries to what is possible with AI to cure debilitating diseases.
Article written by Nic Fleming on "How artificial intelligence is changing drug discovery". Machine learning and other technologies are expected to make the hunt for new pharmaceuticals quicker, cheaper and more effective.
There is plenty of guidance and tips online for PhD candidates, for example in the FindaPhD website where our programme will be advertised.