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on the adsorption mechanisms of these molecules on the metallic surfaces. In this PhD project we will use state-of-art molecular simulation methods [2,3] to clarify the adsorption and desorption mechanisms of various
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About the project: Fast and Flexible Nested Sampling via Collective Move Dynamics for Molecular Systems Supervisor: Dr Livia Bartók-Pártay, University of Warwick Organic matter encompasses a range
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detection by mass spectrometry, by combining molecular dynamics simulations and experiments (Figure 1).1-5 The present PhD thesis will investigate the effects of additional specific ions in fluid samples
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molecular docking, molecular dynamics and free-energy methods (MD/FEP), machine learning for molecular design, and protein–ligand modelling. Experience bridging computational and experimental groups, and the
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collaborate closely with experts in microbiology, biophysics, microscopy, image analysis, and molecular biology. Requirements PhD degree, or a foreign degree equivalent to a PhD degree, in systems biology
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l'ENS, within a research environment specialized in soft matter, statistical physics, and fluid dynamics. The project will rely primarily on large-scale molecular dynamics simulations and theoretical
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education to enable regions to expand quickly and sustainably. In fact, the future is made here. The Department of Molecular Biology/Umeå university, are searching for a DDLS PhD student in Data driven
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and molecular function. The project will build on the MDSPACE image analysis software [2], and its application to characterising VCP/p97 dynamics [3], to analyse cryo‑electron microscopy (cryo-EM
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systems (LIED, Université Paris Cité), experts in classical atomistic simulations (molecular dynamics and coarse-graining, LMCE, CEA/DAM/DIF), as well as specialists in continuum simulations (finite element
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 13 days ago
macromolecular dynamics, as well as collaborations with the European MDDB initiative. The candidate will develop new graph transformer architectures to learn conformational heterogeneity from molecular dynamics