Postdoctoral Position - Deep Learning applied to Molecular Simulations at the Computational Science...

Postdoctoral scientist to lead a research line in building and using machine learning potentials for molecular simulations. The candidate will contribute together with other PhDs fellows to the following research line of the Computational Science Laboratory

Research line: Molecular simulations and machine learning. We use computation such as physics-based simulations and modern machine learning to provide novel, innovative methodological approaches in biomedicine. Specifically, we develop new methods that can be applied to drug design, protein dynamics, protein-protein interactions, etc. For this, we created GPUGRID.net in 2008, currently the second largest distributed computing project harnessing several thousands GPUs. In 2017 we created PlayMolecule, a publicly-available platform offering molecular simulations and machine-learning-powered assets for drug discovery used by a wide community of users (>100k jobs reached, >6000 unique users). The platform has been successfully tested in blind challenges (D3R challenge 2018, Sampl 2019) and it is currently deployed internally in several top-10 pharmaceutical companies. Since 2019, we are core project leaders of OpenMM/ACEMD, one of the leading molecular dynamics packages, jointly with the University of Stanford and the Memorial Sloan Kettering Cancer Center (NY,USA) where we are responsible for the development of machine learning potentials between quantum and classical mechanics and end-to-end simulation approaches. This year we have released TorchMD, a framework for molecular simulations that enables users to do research faster in force-field development as well as integrate neural network potentials seamlessly into the dynamics with the simplicity and power of PyTorch. Relevant References: https://scholar.google.es/citations?hl=en&user=-_kX4kMAAAAJ&view_op=list...