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PhD Programme in Advanced Machine Learning

The Cambridge Machine Learning Group (MLG) runs a PhD programme in Advanced Machine Learning. The supervisors are Jose Miguel Hernandez-Lobato , Carl Rasmussen , Richard E. Turner , Adrian Weller , Hong Ge and David Krueger . Zoubin Ghahramani is currently on academic leave and not accepting new students at this time.

We encourage applications from outstanding candidates with academic backgrounds in Mathematics, Physics, Computer Science, Engineering and related fields, and a keen interest in doing basic research in machine learning and its scientific applications. There are no additional restrictions on the topic of the PhD, but for further information on our current research areas, please consult our webpages at http://mlg.eng.cam.ac.uk .

The typical duration of the PhD will be four years.

Applicants must formally apply through the Applicant Portal at the University of Cambridge by the deadline, indicating “PhD in Engineering” as the course (supervisor Hernandez-Lobato, Rasmussen, Turner, Weller, Ge and/or Krueger). Applicants who want to apply for University funding need to reply ‘Yes’ to the question ‘Apply for Cambridge Scholarships’. See http://www.admin.cam.ac.uk/students/gradadmissions/prospec/apply/deadlines.html for details. Note that applications will not be complete until all the required material has been uploaded (including reference letters), and we will not be able to see any applications until that happens.

Gates funding applicants (US or other overseas) need to fill out the dedicated Gates Cambridge Scholarships section later on the form which is sent on to the administrators of Gates funding.

Deadline for PhD Application: noon 5 December, 2023

Applications from outstanding individuals may be considered after this time, but applying later may adversely impact your chances for both admission and funding.

FURTHER INFORMATION ABOUT COMPLETING THE ADMISSIONS FORMS:

The Machine Learning Group is based in the Department of Engineering, not Computer Science.

We will assess your application on three criteria:

1 Academic performance (ensure evidence for strong academic achievement, e.g. position in year, awards, etc.) 2 references (clearly your references will need to be strong; they should also mention evidence of excellence as quotes will be drawn from them) 3 research (detail your research experience, especially that which relates to machine learning)

You will also need to put together a research proposal. We do not offer individual support for this. It is part of the application assessment, i.e. ascertaining whether you can write about a research area in a sensible way and pose interesting questions. It is not a commitment to what you will work on during your PhD. Most often PhD topics crystallise over the first year. The research proposal should be about 2 pages long and can be attached to your application (you can indicate that your proposal is attached in the 1500 character count Research Summary box). This aspect of the application does not carry a huge amount of weight so do not spend a large amount of time on it. Please also attach a recent CV to your application too.

INFORMATION ABOUT THE CAMBRIDGE-TUEBINGEN PROGRAMME:

We also offer a small number of PhDs on the Cambridge-Tuebingen programme. This stream is for specific candidates whose research interests are well-matched to both the machine learning group in Cambridge and the MPI for Intelligent Systems in Tuebingen. For more information about the Cambridge-Tuebingen programme and how to apply see here . IMPORTANT: remember to download your application form before you submit so that you can send a copy to the administrators in Tuebingen directly . Note that the application deadline for the Cambridge-Tuebingen programme is noon, 5th December, 2023, CET.

What background do I need?

An ideal background is a top undergraduate or Masters degree in Mathematics, Physics, Computer Science, or Electrical Engineering. You should be both very strong mathematically and have an intuitive and practical grasp of computation. Successful applicants often have research experience in statistical machine learning. Shortlisted applicants are interviewed.

Do you have funding?

There are a number of funding sources at Cambridge University for PhD students, including for international students. All our students receive partial or full funding for the full three years of the PhD. We do not give preference to “self-funded” students. To be eligible for funding it is important to apply early (see https://www.graduate.study.cam.ac.uk/finance/funding – current deadlines are 10 October for US students, and 1 December for others). Also make sure you tick the box on the application saying you wish to be considered for funding!

If you are applying to the Cambridge-Tuebingen programme, note that this source of funding will not be listed as one of the official funding sources, but if you apply to this programme, please tick the other possible sources of funding if you want to maximise your chances of getting funding from Cambridge.

What is my likelihood of being admitted?

Because we receive so many applications, unfortunately we can’t admit many excellent candidates, even some who have funding. Successful applicants tend to be among the very top students at their institution, have very strong mathematics backgrounds, and references, and have some research experience in statistical machine learning.

Do I have to contact one of the faculty members first or can I apply formally directly?

It is not necessary, but if you have doubts about whether your background is suitable for the programme, or if you have questions about the group, you are welcome to contact one of the faculty members directly. Due to their high email volume you may not receive an immediate response but they will endeavour to get back to you as quickly as possible. It is important to make your official application to Graduate Admissions at Cambridge before the funding deadlines, even if you don’t hear back from us; otherwise we may not be able to consider you.

Do you take Masters students, or part-time PhD students?

We generally don’t admit students for a part-time PhD. We also don’t usually admit students just for a pure-research Masters in machine learning , except for specific programs such as the Churchill and Marshall scholarships. However, please do note that we run a one-year taught Master’s Programme: The MPhil in Machine Learning, and Machine Intelligence . You are welcome to apply directly to this.

What Department / course should I indicate on my application form?

This machine learning group is in the Department of Engineering. The degree you would be applying for is a PhD in Engineering (not Computer Science or Statistics).

How long does a PhD take?

A typical PhD from our group takes 3-4 years. The first year requires students to pass some courses and submit a first-year research report. Students must submit their PhD before the 4th year.

What research topics do you have projects on?

We don’t generally pre-specify projects for students. We prefer to find a research area that suits the student. For a sample of our research, you can check group members’ personal pages or our research publications page.

What are the career prospects for PhD students from your group?

Students and postdocs from the group have moved on to excellent positions both in academia and industry. Have a look at our list of recent alumni on the Machine Learning group webpage . Research expertise in machine learning is in very high demand these days.

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Machine Learning and Artificial Intelligence

Join our Machine Learning Lab for a PhD at the University of Cambridge!

Ready to push the boundaries of research, as a part of a dynamic and world-renowned group dedicated to harnessing the power of Machine Learning to tackle real-world challenges?

We are thrilled to announce that  the van der Schaar lab  is actively seeking 5 exceptional PhD students, with a keen interest in the following areas:

• Human-AI alignment,
• Foundational Models,
• AI for Scientific Discovery,
• Reinforcement and Inverse Reinforcement Learning,
• Synthetic Data and Simulators.

All available positions are fully-funded. We are committed to fostering a diverse and inclusive research environment, and funding is available to both home and international students.

Applications are reviewed on a rolling basis, so we encourage you to seize this opportunity and apply as soon as possible. For detailed information about our recruitment process and to submit your application, please visit:  https://www.vanderschaar-lab.com/join-the-van-der-schaar-lab/ .

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The University of Cambridge is one of the world’s top five universities, and is home to a vibrant community of researchers and educators. The University provides a range of courses relating to AI, its use across disciplines, and its critical evaluation. Explore where you can find AI in our courses at the links below.

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PhD in Computer Science

• Undergraduate admissions
• MPhil in Advanced Computer Science

Potential applicants should refer to the the Postgraduate Admissions Course Directory for information about the program and application requirements.

Those who are applying for one of the department's PhD studentships and RA'ships , you should use the deadline published on the individual job listing.

For those applicants who have not secured external scholarships and who wish to be considered for the various University and Cambridge Trusts' funding competitions, applications for the academic year commencing October 2025, and January 2026, open on 3 September 2024. Applicants are encouraged to apply early. The deadline for submission of complete applications is 23:59 (GMT) on  3 December 2024 . Applicants who have secured external funding may apply up to 15 May 2025.

Please note that applications submitted between 4 December 2024 and 15 May 2025 will be considered only if there is a named potential supervisor who has invited the application, if the application is complete, and if funding is readily available. Note that incomplete applications received after this date will only be considered for January 2026 admission but cannot be considered for the funding competitions (for which the deadline is 3 December 2024).

Applicants should refer to the Postgraduate Admissions page for links to the application portal , deadlines, guidance and information.

Please note the following:

• Applications for funding support from the University and Cambridge Trusts must be submitted early: see University funding deadlines .
• Research students are normally admitted to the probationary Certificate of Postgraduate Studies in Computer Science (see CPGS) in the first instance.
• Applicants will also need to include a brief research abstract within the section of the application form which asks for a research topic and to indicate a potential supervisor's name .
• Applicants should discuss their proposed research with a potential supervisor before submitting the application for admission. See Research proposal for further details about the research proposal, and Research themes for links to potential supervisors within areas of your research interests. Please confirm that you have discussed the project with the proposed supervisor, and that they have confirmed they will be content to review an application from you. You should include a statement to this effect within the research abstract section of the application form.
• Applications are considered as they are received.

Please note that your application can only be considered by the department once it has been submitted. Your application can only be submitted if it is complete. You will be able to upload all your supporting material including a research proposal and the same time as submitting the application form. It is therefore very important to have all your supporting material, including agreement from your referees to provide you with references, your transcripts and research project proposal, ready before you start the application.

Applicants wishing to be considered for funding competitions should check their eligibility on the University-wide Sources of Funding web page.

The application portal acts as a scholarship funding application as well as an application for admission. In most cases, no further funding application form is required. There are some exceptions, however: we recommend checking the Student Funding webpage which provides information about other grants for students applying to Cambridge and their closing dates.

The current costs of a PhD are available from the Student Registry's Postgraduate Course Costs and Fee Status page . You will need sufficient funding to cover the University Tuition Fee, and at least the minimum maintenance for three years.

Once again, if you wish to apply for one of the department's PhD studentships and RA'ships , you should use the deadline published on the individual job listing.

Home students

The Department makes awards to UK students both from funds supplied, for example the EPSRC Doctoral Training Grant, and from its own funds such as the Premium Studentship  and the Hopper Studentship . In the year starting October 2023, the Department will help to fund up to three research students from its Doctoral Training Grant. These funds are limited. The Applications Panel considers all successful applicants for funding awards within its gift and submits the names of highly ranked home and international students to the Cambridge Trusts.

The Department may also have positions associated with industrial collaborations and particular research projects. Such studentships are advertised on the University's Jobs web page.

Very highly ranked international students will be considered for nomination to the Gates Cambridge Trust and Cambridge International Scholarship Scheme ( CISS ) competitions.

Please note earlier applications deadline for Gates Cambridge US scholarships for US students who are resident in the US: 11 October 2023 .

The Department will contact applicants directly about its internal awards such as the Premium Studentship .

It is worth noting that full funding must be secured before starting a course at Cambridge. Most of the scholarships will not accept applications from students who are already in residence. We strongly discourage students asserting they can self-fund a PhD in the hope that something else will turn up once you are in Cambridge.

Admission conditions

The Faculty's Degree Committee makes recommendations to offer places to successful applicants via the University's central Postgraduate Admissions Office, the only body with the authority to make an offer of a place as a postgraduate student. Offers from Postgraduate Admissions are usually conditional . A deadline will be set by which date all conditions must be met and, once met, the offer is confirmed by the Postgraduate Admissions Office.

Standard conditions include securing sufficient funding for three years to meet the financial conditions of the University including tuition fees and maintenance, and College membership. Additionally, applicants may be asked to achieve a certain grade in their current studies; to take or retake an English Language proficiency test ; and any other conditions the Postgraduate Admissions Office might apply.

Successful applicants who have applied online may be required to send original documents to the University's Postgraduate Admissions Office for validation. We strongly recommend the use of a reputable courier and that you obtain a tracking number .

CDT in Decision Making in Complex Systems

The AI CDT in Decision Making for Complex Systems is a programme offered in conjunction with the University of Manchester that aims to enable students to develop new fundamental AI capabilities in the context of a diversity of complex systems. Rather than working in isolation, as is usual in AI,  the students will learn to develop these in a collaborative manner tied to a specific application domain. The CDT is focused on three areas, Uncertainty in complex systems, Decision-making with humans in the loop and Decision-making for ML systems. Model interpretability and explainability will be transversal to the three topics. Decision making with AI needs  to be interpretable and explainable to facilitate interrogation of decision processes such that trust can be built by the human, and it is essential for understanding and meeting ethical and legal implications.

Like all research students admitted to read for the PhD degree, those admitted to the AI CDT in Decision Making for Complex Systems are admitted on a probationary basis. They will have successfully completed the Postgraduate Diploma in Artificial Intelligence  at the University of Manchester before being registered on a probationary basis at the University of Cambridge. During this year students may do some additional coursework and will write a research report that is likely to form the foundation of the eventual PhD thesis. Applications for admission in Michaelmas 2025 open in September 2024.

Please contact the department's Postgraduate Education Manager with any questions not answered above.

Email: Postgraduate Education Manager

Include "PhD application query" in the subject.

Department of Computer Science and Technology William Gates Building 15 JJ Thomson Avenue Cambridge CB3 0FD

Tel: +44 1223 334656 (NB may not be accessible during remote working)

Postgraduate Admissions Office Academic Division Student Services Centre Bene't Street, New Museums Site Cambridge, CB2 3PT, U.K.

WWW: https://www.postgraduate.study.cam.ac.uk/

Department of Computer Science and Technology University of Cambridge William Gates Building 15 JJ Thomson Avenue Cambridge CB3 0FD

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Cambridge Centre for AI in Medicine announces its official launch

The University of Cambridge has announced a five-year agreement with AstraZeneca and GSK to fund the Cambridge Centre for AI in Medicine (CCAIM). For the five-year duration, AstraZeneca and GSK will support five new PhD studentships per year. This programme will enable the best and brightest young minds in machine learning and bioscience to partner with leaders in industry and academia, wherever they may be in the world.

CCAIM is designed to break down the barriers between machine learning and medical science, to create a unique forum in which we can work together to truly understand the challenges, formalise the problems, and develop practical solutions that can be readily implemented in healthcare. Professor Mihaela van der Schaar

CCAIM has been set up as a cutting-edge research group. Its faculty of 10 University of Cambridge researchers – in addition to world-class PhD students, currently being recruited – have united to develop AI and machine learning (ML) technologies aiming to transform clinical trials, personalised medicine and biomedical discovery.

The centre’s Director is Professor Mihaela van der Schaar , a world leading researcher in ML, and the Co-Director is researcher-clinician Professor Andres Floto. The faculty also includes Dr Sarah Teichmann FMedSci FRS, Head of Cellular Genetics at the Wellcome Sanger Institute and founder and principal leader of the Human Cell Atlas international consortium.

Successfully bridging the gap between the disparate and complex fields of AI and medicine requires building from both sides simultaneously. CCAIM brings together a diverse coalition of leading Cambridge scientists and clinicians, with expertise in machine learning, engineering, mathematics, medicine, computer science, genetics, computational biology, biostatistics, clinical research, healthcare policy and more.

These multi-disciplinary experts from the University of Cambridge will work in close collaboration with scientists and leaders from AstraZeneca and GSK to identify critical challenges facing drug discovery and development that have the potential to be solved through cutting-edge academic research.

The Centre’s research output and the implementation of its ML tools could be transformational not only for the pharmaceutical industry – including in clinical trials and drug discovery – but also for the clinical delivery of healthcare to patients. The CCAIM team already has deep research links with the NHS, and four of the Centre’s members are NHS doctors.

Professor van der Schaar said: “Machine learning has the potential to truly revolutionise the delivery of healthcare, to the great benefit of patients, clinicians and the wider medical ecosystem. But to realise this potential requires true and deep cross-disciplinary understanding – a great challenge because we speak different languages. CCAIM is designed to break down the barriers between machine learning and medical science, to create a unique forum in which we can work together to truly understand the challenges, formalise the problems, and develop practical solutions that can be readily implemented in healthcare.”

Professor Andre Floto said: “We are thrilled that the CCAIM is taking off. From tackling the immediate threats of COVID-19 , to the long-term transformation of healthcare systems, our network of experts and incoming PhD students will bring next-level AI to bear on the most pressing medical issues of our time.”

Professor Andy Neely OBE , Pro-Vice-Chancellor for Enterprise and Business Relations, University of Cambridge, said: “The CCAIM is a terrific and timely venture that builds on the strong relationships between the University of Cambridge and global leaders in the pharmaceutical industry, AstraZeneca and GSK. The depth and diversity of the CCAIM faculty’s expertise means it is uniquely positioned to deliver and accelerate the breakthroughs in medical science and healthcare that AI has long promised. I anticipate the Centre’s impact will be nothing less than transformational.”

Jim Weatherall, Vice President, Data Science & AI, R&D, AstraZeneca, said: “We know the best science doesn’t happen in isolation which is why collaboration is essential to the way we work. This new centre combines world class academia with real-world industrial challenges and will help to develop cutting-edge AI to potentially transform the way we discover and develop medicines.”

Kim Branson, Senior Vice President and Global Head of AI/ML, GSK, said: “The new CCAIM will recruit and train the next generation of practitioners at the intersection of AI, industry and academia. The work of this institute will be critical to translating AI methods from theory to practice, so that we can keep improving our therapeutic discovery efforts and so that together we can make a tangible impact on patients, from diagnosis, to treatment and beyond.”

Biographies

Professor Mihaela van der Schaar, CCAIM Director

Mihaela van der Schaar is John Humphrey Plummer Professor of Machine Learning, Artificial Intelligence and Medicine at the University of Cambridge, where she directs the Cambridge Centre for AI in Medicine and heads up the van der Schaar Lab. In 2019, National Endowment for Science, Technology and the Arts (NESTA), identified Professor van der Shaar as the most-cited female AI researcher in the UK. In 2020, she was among the top 10 authors not only at ICML, but also at NeurIPS, two of the world’s most prestigious machine learning conferences. Professor van der Schaar is also a Turing Faculty Fellow at The Alan Turing Institute in London, a Chancellor’s Professor at UCLA and an IEEE Fellow. A fuller biography, including information on Professor van der Schaar’s many awards and patents, is available here .

Professor Andres Floto, CCAIM Co-Director

Andres Floto is a Professor of Respiratory Biology at the University of Cambridge, a Wellcome Trust Senior Investigator, and Research Director of the Cambridge Centre for Lung Infection at Papworth Hospital, Cambridge. Clinically, he specialises in the treatment of patients with Cystic Fibrosis (CF), non-CF bronchiectasis, and infections with nontuberculous mycobacteria.

Professor Floto research explores how immune cells interact with bacteria, how intracellular killing and inflammation are regulated and sometimes subverted during infection, how population-level whole-genome sequencing can be used to reveal biology of bacterial infection, and how therapeutic enhancement of cell-autonomous immunity may provide novel strategies to treat multi-drug-resistant pathogens.

Dr James Weatherall, Vice President, Data Science & AI, R&D, AstraZeneca

Since joining AstraZeneca in 2007, Dr Weatherall has held diverse roles focused on driving the application of data science, artificial intelligence, advanced analytics and related approaches to unlock the full potential of data – transforming the way medicines are discovered and developed and making a difference to patients’ lives. Dr Weatherall is an Honorary Reader in Computer Science at the University of Manchester and Vice-Chair of the Data Science Section at the Royal Statistical Society. He has contributed to and published in diverse fields such as data visualisation, cryptography, text mining, machine learning and health data science.

Dr Kim Branson, Senior Vice President and Global Head of AI/ML, GSK

Dr Kim Branson leads all GSK’s AI/ML initiatives and projects. Dr Branson has been involved in large scale machine learning and medical informatics initiatives for more than 15 years, over a range of ventures from computational drug design to disease risk prediction. 

Dr Branson received degrees from the University of Adelaide, and a PhD from the University of Melbourne. 

This article first appeared on the CCAIM website .

The text in this work is licensed under a  Creative Commons Attribution-NonCommercial-ShareAlike 4. 0 International License . Images, including our videos, are Copyright ©University of Cambridge and licensors/contributors as identified.  All rights reserved. We make our image and video content available in a number of ways that permit your use and sharing of our content under their respective Terms.

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Artificial Intelligence Group

The work of the Artificial Intelligence Group is multi-disciplinary, spanning genomics and bio-informatics, computational learning theory, computer vision, and informal reasoning. A unifying theme is understanding multi-scale pattern recognition problems, seeking powerful (often statistical) algorithms for modeling and solving them, and for learning from data. The AI Group seeks to find synergies amongst ideas based in statistics, mechanised reasoning, cognitive science, biology, and engineering, and to develop practical applications from them.

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MPhil in Machine Learning and Machine Intelligence

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The goal of the field of Machine Intelligence is to develop systems that can perceive the world, plan and make decisions, interact with humans and other intelligent agents, and provide explanations for their actions. Machine Learning provides many of the technical tools used to develop intelligent systems. This field overlaps with statistics and computer science.

The MPhil in Machine Learning and Machine Intelligence is an eleven month full-time programme offered by the Machine Learning Group, the Speech Group, and the Computer Vision and Robotics Group in the Cambridge University Department of Engineering.  The course aims to teach the state-of-the-art in machine learning, speech and language processing, and computer vision; to give students the skills and expertise necessary to take leading roles in industry and to equip them with the research skills necessary for doctoral study at Cambridge and other universities. Employment prospects are also extremely good for students who plan to go directly into industry. The course will impart directly employable skills and expertise which are in great demand in the IT, financial, and manufacturing sectors. 

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About the Faculty of Philosophy

Philosophy has a long tradition in the University, with the Moral Sciences Tripos (renamed 'Philosophy' in 1970) being taught since 1852. Cambridge was the birthplace of 'analytic' philosophy, and the philosophical home of Russell, Moore, Ramsey, Wittgenstein and Anscombe, as well as many other distinguished contributers to the philosophy of the twentieth century. Today the Faculty continues to be a world-class centre for philosophical research. The Philosophy Faculty admits about 15 students a year for the MPhil. They join 20-25 students studying for the PhD and MLitt to form a lively graduate community. A wide range of seminars and informal gatherings ensures that students get to know each other, making the Faculty a friendly, informal and congenial place to work. The Faculty has been extraordinarily successful in placing students in academic jobs and former students have teaching posts in universities throughout the UK and beyond (see Faculty Placement Record). http://www.phil.cam.ac.uk/prosp-students/prosp-grad-placement

The Faculty is situated on Sidgwick Avenue, close to many of the Faculties with which is has close links, and the University Library is only 500m away. The Faculty's accommodation includes a Graduate Centre and Common Room, as well as our own Library, holding some 16,000 books and three dozen current journals.

4 courses offered in the Faculty of Philosophy

Ai ethics and society - mst.

Artificial intelligence (AI) technology is rapidly developing and is increasingly being applied across sectors, posing significant ethical and societal challenges. There is therefore a national and global need to  adequately equip future leaders and decision-makers to address these challenges.

The Masters of Studies in AI Ethics and Society addresses this need. The MSt is an academically rigorous part-time programme aimed at professionals from business, public, and social sectors working with AI. The programme will provide students with the critical skills, knowledge and analytical abilities needed to identify and address ethical challenges as they arise in practice from the application of AI. The MSt will engage with the ethical and societal challenges of AI  and is thoroughly informed by the knowledge, theories and methods of established academic disciplines from philosophy to computer science.

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Ethics of AI, Data and Algorithms - MPhil - Closed

The MPhil in Ethics of AI, Data and Algorithms is a full-time 9-month course run by the Leverhulme Centre for the Future of Intelligence. It equips students from a range of backgrounds with the research skills and specialist knowledge to engage critically and constructively with debates on the ethical and societal impacts of AI and other digital technologies, and provides the opportunity to carry out focused research under close supervision by domain experts at the University. Those intending to go on to doctoral work learn the research skills needed to help them prepare a well-planned and focused PhD proposal.

In addition to individual supervisions that support work on essays and dissertation, the taught elements of the course consist of core seminars, which introduce the central topics in AI and data ethics, a range of elective modules covering specialist topics, and work in progress seminars, in which students gain experience in presenting their own work and discussing the issues that arise from it with an audience of their peers and senior members of staff. Students also have the opportunity to attend lectures, research seminars and reading groups across the Centre and the wider University.

Philosophy - MPhil - Closed

The MPhil in Philosophy is a full-time course, introducing students to the skills needed in philosophical research. Students work with supervisors to write two research essays: the first of up to 4,000 words, the second of up to 8,000 words; and a dissertation of up to 12,000 words. Students also take part as a group in a collaborative weekly seminar, run during Michaelmas and Lent Terms, in which they learn presentation and discussion skills by presenting their own research and by discussing presentations by other students.

Philosophy - PhD - Closed

The PhD degree is awarded for an extended thesis which makes a substantial original contribution to learning.

1 course also advertised in the Faculty of Philosophy

Philosophy - pgcert.

From the Institute of Continuing Education

The post graduate Certificate in Philosophy is a part-time Postgraduate Certificate equivalent to 60 credits at FHEQ level 7. It is undertaken over one year. Students are taught a range of general and subject-specific skills and techniques.

Delivery of the course is via three Units. The Units are structured chronologically—spanning philosophical thought, ancient to modern. The Units are also developmental—discussions in subsequent Units build on and respond to themes discussed in previous Units. The units cover the following three topics:  Ancient Greek Philosophy, Early Modern Philosophy and Existentialism. 

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Cambridge centre for data-driven discovery, warning message, fully-funded (home rate) phd studentship (fixed term): ai forensics.

Faculty of English

Cambridge Digital Humanities invites applications for a funded 3-year PhD studentship to conduct research within the AI Forensics project, funded by the Volkswagen Foundation. For more information on the project, please see the project description on the CDH website:  https://www.cdh.cam.ac.uk/research/projects/ai-forensics/

The PhD will be hosted by the Faculty of English, but applications are welcome from a wide range of technical and humanities disciplines. Applicants are invited to propose their own PhD project within the project description and in line with their own research interests and expertise. Please note that if successful, an applicant's research plan will have to be reviewed after the start of the PhD, in terms of the objectives of the overall research project.

Applicants would preferably have a background in either a relevant humanities discipline (e.g. history of art, history of science, digital humanities) and/or a relevant technical discipline (e.g. machine learning, image processing). Since both fields are relevant to the project, ideal applicants would have an interest in (or willingness to learn) any relevant areas they haven't yet formally studied. Applicants should have (or expect to be awarded) an upper 2nd or 1st class honours degree, or equivalent international qualification.

https://www.jobs.cam.ac.uk/job/36519/

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The future of intelligence: Cambridge University launches new centre to study AI and the future of humanity

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The University of Cambridge is launching a new research centre, thanks to a £10 million grant from the Leverhulme Trust, to explore the opportunities and challenges to humanity from the development of artificial intelligence. 

Machine intelligence will be one of the defining themes of our century, and the challenges of ensuring that we make good use of its opportunities are ones we all face together Huw Price

Human-level intelligence is familiar in biological “hardware” – it happens inside our skulls. Technology and science are now converging on a possible future where similar intelligence can be created in computers.

While it is hard to predict when this will happen, some researchers suggest that human-level AI will be created within this century. Freed of biological constraints, such machines might become much more intelligent than humans. What would this mean for us? Stuart Russell, a world-leading AI researcher at the University of California, Berkeley, and collaborator on the project, suggests that this would be “the biggest event in human history”. Professor Stephen Hawking agrees, saying that “when it eventually does occur, it’s likely to be either the best or worst thing ever to happen to humanity, so there’s huge value in getting it right.”

Now, thanks to an unprecedented £10 million grant from the Leverhulme Trust , the University of Cambridge is to establish a new interdisciplinary research centre, the Leverhulme Centre for the Future of Intelligence, to explore the opportunities and challenges of this potentially epoch-making technological development, both short and long term.

The Centre brings together computer scientists, philosophers, social scientists and others to examine the technical, practical and philosophical questions artificial intelligence raises for humanity in the coming century.

Huw Price, the Bertrand Russell Professor of Philosophy at Cambridge and Director of the Centre, said: “Machine intelligence will be one of the defining themes of our century, and the challenges of ensuring that we make good use of its opportunities are ones we all face together. At present, however, we have barely begun to consider its ramifications, good or bad”.

The Centre is a response to the Leverhulme Trust’s call for “bold, disruptive thinking, capable of creating a step-change in our understanding”. The Trust awarded the grant to Cambridge for a proposal developed with the Executive Director of the University’s Centre for the Study of Existential Risk ( CSER ), Dr Seán Ó hÉigeartaigh. CSER investigates emerging risks to humanity’s future including climate change, disease, warfare and technological revolutions.

Dr Ó hÉigeartaigh said: “The Centre is intended to build on CSER’s pioneering work on the risks posed by high-level AI and place those concerns in a broader context, looking at themes such as different kinds of intelligence, responsible development of technology and issues surrounding autonomous weapons and drones.”

The Leverhulme Centre for the Future of Intelligence spans institutions, as well as disciplines. It is a collaboration led by the University of Cambridge with links to the Oxford Martin School at the University of Oxford, Imperial College London, and the University of California, Berkeley. It is supported by Cambridge’s Centre for Research in the Arts, Social Sciences and Humanities ( CRASSH ). As Professor Price put it, “a proposal this ambitious, combining some of the best minds across four universities and many disciplines, could not have been achieved without CRASSH’s vision and expertise.”

Zoubin Ghahramani, Deputy Director, Professor of Information Engineering and a Fellow of St John’s College, Cambridge, said:

“The field of machine learning continues to advance at a tremendous pace, and machines can now achieve near-human abilities at many cognitive tasks—from recognising images to translating between languages and driving cars. We need to understand where this is all leading, and ensure that research in machine intelligence continues to benefit humanity. The Leverhulme Centre for the Future of Intelligence will bring together researchers from a number of disciplines, from philosophers to social scientists, cognitive scientists and computer scientists, to help guide the future of this technology and  study its implications.”

The Centre aims to lead the global conversation about the opportunities and challenges to humanity that lie ahead in the future of AI. Professor Price said: “With far-sighted alumni such as Charles Babbage, Alan Turing, and Margaret Boden, Cambridge has an enviable record of leadership in this field, and I am delighted that it will be home to the new Leverhulme Centre.”

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