Ph.D. student
Type of contract: Temporary
Job status: Part-time
Starting date: October 2024
The Department of Computational Chemistry of the J. Heyrovský Institute of Physical Chemistry in Prague offers two part-time positions for Ph.D. students focusing on the development of machine-learning potentials for molecular simulations.
Description
Machine learning offers a promising alternative to traditional approaches to coarse graining the description of biomolecular systems and their interactions. Due to the ability of machine learning potentials to describe an arbitrary shape of effective potential surface, one does not have to assume any particular functional form of the emergent interaction potentials. Especially at more extreme levels of coarse graining, this allows for the targeting of specific all-atom reference data, rather than the use of simplified models. In this project, the aim is to develop effective machine learned potentials for the interaction of small solutes with proteins and their aggregates based on provided reference all-atom data and previous coarse-grained models for the proteins themselves.
Requirements
- Good knowledge of machine learning principles and techniques
- Experience with the construction of equivariant message passing machine learning potentials
- Experience with training set construction and active learning techniques, including query by committee
- Experience with running parallel and GPU HPC jobs
- Ability to adapt existing model architectures to the systems at hand
- Python programming language, including data analysis and plotting
- Experience with Nequip or MACE
To apply, please send your CV, a motivation letter, and contacts for two references, including the following text in the subject line of your email: SC2024_31.
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