PhD in Machine Learning at the Van de Schaar lab at University of Cambridge | FULLY-FUNDED

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There is room for four outstanding machine learning Ph.D. students to join an elite group at the University of Cambridge.

5 ways we can empower you

1. Fully funded
All positions are fully funded for 4 years, thanks to the generous support of partners as diverse as AstraZeneca, AVIVA, the Cystic Fibrosis Trust, GlaxoSmithKline, Illumina, Microsoft Research, and the Office of Naval Research, among others. Funding covers both home and international fees in addition to living costs/stipend.

2. World-leading research and tuition
Mihaela van der Schaar is one of the foremost names in machine learning, and the University of Cambridge is the absolute pinnacle of academia.

We’re a small group with under 20 researchers, but we punch well above our weight. Our lab has had 27 papers accepted at the four largest AI and machine learning conferences (NeurIPS, AISTATS, ICLR, and ICML) in the last year alone.

3. Freedom to think big and explore
We are creating new frontiers in machine learning. Despite our primary focus on medicine, we produce ground-breaking work across an enormous range of machine learning sub-fields – including deep learning, causal inference, AutoML, time-series analysis, reinforcement learning, and many more.

This breadth of expertise is a product of our exceptional academic diversity: among our researchers, we have computer scientists, engineers, applied mathematicians, statisticians, econometricians, and physicists.

4. Projects with a purpose; work that can change the world
Our research projects are targeted and practical. Our mission is to apply machine learning to real-world problems in healthcare, and our goal is nothing short of a revolution in medicine.

5. Unmatched prospects
Employers know that our lab only takes the best and brightest. When you graduate,  you won’t need to settle: our alumni around the world have become leaders in their fields, with some continuing to full professorships and others joining top private-sector teams including DeepMind, Intel, Qualcomm and Apple.

Eligibility and application process

We expect to be recruiting from late 2022 through early 2023.

Admission is competitive, and successful candidates will need to have top grades from world-leading academic institutions, with excellent mathematical backgrounds and preferably experience in machine learning. No knowledge of medicine or biology is required.

Applicants should hold (or be predicted to achieve) the equivalent of a first-class undergraduate degree with honours in a course with a strong quantitative component (e.g. engineering, comp sci, mathematics, economics, etc.) and preferably a master’s degree.

The lab views diversity of all kinds as a crucial source of strength and creativity. To that end, applications are sought from candidates with a wide variety of nationalities, academic specialisations, and professional backgrounds. This is reflected in the previous year’s intake of 6 PhD students, as well as the composition of the broader research team.

Please contact us using the form below, uploading your resume and providing links to any relevant publications, reports or code if these are available.

We’ll make every effort to review your application, but please note that this may take a while (due to the volume of applications, combined with internal requirements regarding timeframes for evaluating and accepting new researchers).

Please also note that we will only contact you if we plan to follow up (i.e. learn more about you or invite you to interview). 

Additionally, note that you do not need to submit your information via the University of Cambridge’s website unless we have contacted you to follow up on your application.

Interested in joining the lab? Sign up HERE!


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