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Postgraduate Admissions

 

Fixed-term: The funds for this post are available for 3 years in the first instance. The student must complete the programme in this timeframe.

Applications are invited for a fully funded PhD studentship in Dr Alpha Lee's group (aal44@cam.ac.uk) on molecular design and understanding chemical reactivity by combining physics with machine learning. The studentship has funding in place for a UK student, though interested non-UK students are encouraged to contact Dr Lee as there are alternative sources of funding available.

The PhD programme associated with this studentship would start in October 2021. The award covers tuition fees (for UK students) and provides a tax-free stipend. The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

The successful candidate should have a good first degree and a Masters pass in a quantitative field (e.g. physics, chemistry, mathematics, computer science, statistics) that is relevant to the projects. The successful candidate must be highly motivated, capable of performing independent research and have excellent communication skills with the ability to work collaboratively.

Recent technological advances have made high throughput experimentation in chemistry possible. However, the analysis of voluminous and high-dimensional data demands an innovative and novel approach that integrates data into physical theories. We are looking for a PhD student interested in developing machine learning techniques based on physics to accelerate the design-make-test cycle in molecular and materials discovery. We are particularly interested in using machine learning to predict chemical reactivity and design catalysis, as well as designing and understanding electrolytes for energy storage.

This PhD project will build on physics-based machine learning technologies that we have developed, spanning the design-make-test cycle for molecules and materials. For molecules, the group has developed methods for predicting the bioactivity of molecules (https://doi.org/10.1073/pnas.1810847116), predicting the outcomes of chemical reactions (https://doi.org/10.1021/acscentsci.9b00576) and Bayesian design of experiment (https://doi.org/10.1039/C9SC00616H). These methodologies are currently used to power COVID Moonshot, the largest open science effort aiming to find antivirals against COVID19 (https://doi.org/10.1038/s41557-020-0496-2). Similarly, in materials science, the Lee group has developed probabilistic models for materials properties prediction (arXiv:1910.00617), materials synthesis prediction (arXiv:2007.15752), and degradation forecasting (https://doi.org/10.1038/s41467-020-15235-7).

Interested candidates are encouraged to make informal enquiries by contacting Dr. Alpha Lee (aal44@cam.ac.uk).

To make an application, follow the procedure outlined on the University website https://www.graduate.study.cam.ac.uk/how-do-i-apply, selecting the course PhD in Physics and making sure to mention the name of Dr Alpha Lee and the Theory of Condensed Matter group. Awards may also be made to supplement part-support from other sources, and 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 (https://www.winton.phy.cam.ac.uk/jobs/PhD2021 ).

It is IMPORTANT that, when submitting the application, Dr Alpha Lee is also notified through an e-mail to aal44@cam.ac.uk.

Please quote reference KA24702 on your application and in any correspondence about this vacancy.

The University actively supports equality, diversity and inclusion and encourages applications from all sections of society.

The University has a responsibility to ensure that all employees are eligible to live and work in the UK.

Key Information

Department of Physics

Reference: KA24702

Dates and deadlines:

Published
Thursday, 12 November, 2020
Closing Date
Thursday, 3 December, 2020