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computational predictions with experimental validation. At the computational stage, the candidate will establish molecular docking and molecular dynamics simulation workflows to predict receptor–ligand
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and accelerate the development of more high-performing PNSEs. The ultimate goal of the project is to develop, implement, and validate novel deep-learning models for molecular dynamics and coarse-grained
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for generating high‑quality diagnostic images and enabling precise visualization of biological processes at the molecular level. Working in close collaboration with scientific partners, the project aims to refine
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enabling precise visualization of biological processes at the molecular level. Working in close collaboration with scientific partners, the project aims to refine and optimize trapping methodologies
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: OUH - Cell and tissue dynamics (Bøe) Project description GENESIS is a newly established Life Science Convergence Environment that brings active matter physics, cell biology, and machine learning
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docking for ligand optimization • perform extensive molecular dynamics (MD) simulations (AMBER, NAMD, GROMACS) in fully hydrated lipid bilayer systems to replicate physiological conditions and assess
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to identify mutations and small-molecule inhibitors capable of disrupting BAF-centered protein-protein interactions • apply molecular docking (Glide, AutoDOCK) and molecular dynamics simulations (AMBER, NAMD
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, you will be part of a dynamic and interdisciplinary work environment, with ample opportunities for scientific and social input throughout the academic year. This includes training in both social science
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neuroscience, physics and computational science. CINPLA addresses fundamental questions related to learning and neural network dynamics in the healthy and diseased brain and in artificial intelligence systems
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on the historical dynamics of the plague bacterium (Yersinia pestis), including how it might have been transported from outside Europe to Europe during the period between 1300 and 1900 CE. Position as Postdoctoral