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PhD Studentship: Machine Learning for Molecular Modeling

University of Birmingham - School of Mathematics

Qualification Type: PhD
Location: Birmingham
Funding for: UK Students, EU Students, International Students
Funding amount: 100% Home fees covered, and a tax-free annual allowance of £18,622 (UKRI rate for 23/24)
Hours: Full Time
Placed On: 27th November 2023
Closes: 27th February 2024

Project description

This PhD project aims to develop Machine Learning methods for Molecular Modeling with a particular focus on aspects relevant to dynamics preserving coarse-graining strategies. Possible research directions include the following:

Development of methods for automated coarse-graining of molecular systems using equivariant message-passing neural networks. Of particular interest will be the learning of coarse-grained representations with faithful dynamical properties.
Development of computational efficient equivariant representations and inference methods for high-dimensional tensorial quantities such as diffusion and friction tensors for molecular simulation at the mesoscale.
Development and mathematical analysis of Active Learning Strategies for Machine Learning of Interatomic Potentials (MLIP), including improved uncertainty quantification approaches for accurate and computationally efficient estimation of uncertainty in over-parametrized models.
Development of numerical integration methods for accurate and efficient simulation of stochastic machine-learned dynamical models.

During this Ph.D., you will extend your expertise in areas of mathematics (e.g., stochastic modelling, numerical analysis, representation theory) and statistical computing (e.g., Bayesian computation, including Monte Carlo methods) relevant to this project. You will develop your scientific expertise in molecular modelling, and you will extend your expertise in theoretical and practical aspects of Machine Learning in general.

Depending on the research direction, parts of the project may be conducted in collaboration with the groups of Christoph Ortner (Mathematics, University of British Columbia), Reinhard Maurer (Chemistry/Physics, Warwick), or Gabor Csanyi (Engineering, Cambridge).

Award summary

100% Home fees covered, and a tax-free annual allowance of £18,622 (UKRI rate for 23/24). Additional stipends may be available for highly qualified candidates, and interested candidates are encouraged to contact the project supervisor, Dr. Matthias Sachs (m.sachs@bham.ac.uk), to explore additional funding opportunities. Please be aware that in order to receive full consideration for all additional funding opportunities, candidates must submit their application before 23:59, January 14, 2024.

 

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