PhD Studentship in Physics at University of Cambridge

Outstanding international students are invited to apply for a PhD scholarship in computer science at University of Cambridge. The funds for this fellowship are available for 5 years. Candidates are welcome before December 05, 2018.

Applications are invited for a fully funded PhD studentship in Dr Alpha Lee’s Group on molecular design by combining statistical physics with machine-learning for drug discovery. The studentship has NO nationality restrictions, with an expected start date in October 2019.

The award covers tuition fees (for UK/EU/international students) and provides a tax-free stipend of 14,777 p.a. (index linked).

Fixed-term: The funds for this post are available for 3.5 years in the first instance. The student must complete the programme in this timeframe.

We are looking for candidates interested in developing machine-learning techniques based on statistical physics to accelerate the design-make-test cycle in molecular discovery.

The successful candidate should have a good first degree and a Masters in a relevant quantitative field (e.g. physics, chemistry, mathematics, computer science or statistics). The candidate must be highly motivated, capable of performing independent research and have excellent communication skills with collaborative working skills.

Recent technological advances have made high throughput experimentation in chemistry possible. The analysis of voluminous and high-dimensional data demand innovative and novel approaches that integrate data into physical theories.

To accelerate molecular design, we will develop deep learning methods to predict biological activity, making inference on large (potentially noisy and incomplete) pharmacological datasets, refining traditional notions of chemical similarity and pharmacophores to make them statistically powerful. We will also develop scalable Bayesian methods to estimate uncertainties of algorithms and drive experiments using machine-learning models. Accelerating the “make” stage, will require tackling the problem of organic synthesis by developing methods that predict outcomes of organic reactions using quantum chemical descriptors, graph-based machine-learning and data from high throughput synthesis studies. This project builds on our previous work on data-driven models for predicting bioactivity and existing industrial collaborations.

The black box of machine-learning will be unveiled by using statistical physics methods such as including explorations of loss function landscape of machine-learning algorithms trained on chemical/reaction data, developing quantitative methodologies to attribute why machine-learning methods arrived at particular predictions. It is often difficult to disentangle and question implicit assumptions that underlie how human scientists reason. However, a machine-learning algorithm that reaches near-human accuracy, being a mathematical function, can be unpacked and analysed. We believe this will yield new insights about chemistry and the structure of chemical space.

Interested candidates are encouraged to make informal enquiries by contacting Dr. Alpha Lee ( The successful candidate will be expected to meet the graduate admissions entrance requirements of the University of Cambridge and formally apply for admission at

To make an application, follow the procedure outlined on the University website, selecting the course PhD Studentship in Physics and making sure to mention Dr Alpha Lee and Theory of Condensed Matter Group. Awards may also be made to supplement part-support from other sources. Candidates are encouraged to express their interest for other available awards in the application form in addition to this Studentship and also apply to the Winton Scholar programme (

IMPORTANT – when submitting the application, you will need to notify Dr Alpha Lee ( of your submission.

Deadline for submission of the applications is 5th of December 2018.

If you upload any additional documents which have not been requested, we will not be able to consider these as part of your application. Please submit your application by midnight on the closing date.


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