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fundamental research in physics-informed and symmetry-aware machine learning for nonadiabatic excited-state molecular dynamics. Develop and evaluate equivariant graph neural networks and related architectures
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Max Planck Institute for Human Cognitive and Brain Sciences (MPI CBS) | Leipzig, Sachsen | Germany | 3 months ago
networks, and computational modelling. Within the Department, we maintain strong synergies with the Neural Computation Group (PI: Andrej Bicanski ), which develops mechanistic computational models of brain
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Postdoc Position for Computational Genomics, AI Precision Oncology, Cancer Biology and Immunobiology
University of Pittsburgh, Pittsburgh , Pennsylvania, US | Pittsburgh, Pennsylvania | United States | about 2 months agoimmunotherapies, integrating graph neural networks, regulon-aware pooling, and transfer learning with biological regulatory networks. 4) Developing and validating computational biomarkers (IGR burden, TAA burden
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. Research directions. Successful candidates will lead one or more of the following ongoing projects: • Developing neural differential equation and continuous-time dynamical models for spatial and single-cell