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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 19 days ago
project No. 25-14529L - RE-free magnetoelastic materials for efficient and environmentally friendly cooling” in colaboration with Polish experimental partner (Institute of Molecular Physics of the Polish
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. Approaches researched: molecular dynamics, quantum simulations, machine learning/AI, and high-throughput computing. Required: Mgr./MSc. in chemistry, physics, computer science, or a related field If you are
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/Qualifications Experience with molecular simulations (LAMMPS, GROMACS or equivalent) and/or electrostatics simulations (APBS or equivalent). Strong programming skills (Python, FORTRAN, C, or similar). Experience
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, and procedures related to campus safety. Go here to review: https://safety.temple.edu/reports-logs/annual-security-report You may request a copy of the report by calling Temple University’s Campus
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descriptors, molecular simulations, and machine learning, this PhD project seeks to predict ion-exchange isotherm parameters directly from molecular properties. These predictions will be integrated
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measure their mechanical, adhesive and frictional properties. By combining these data with classical molecular dynamics simulations and a molecular-scale approach to the thermodynamics of molecular
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underlying these processes remain poorly represented. This PhD project aims to fill that gap by using molecular simulations to quantify the interactions of SOM and EPS with water and minerals, and to connect
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that directly relate to this position. The Lauren McCormick Lab investigates how the cell's microtubule cytoskeleton assembles, specializes, and evolves using both simulations and experiments. By studying
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S2AQUA – Collaborative Laboratory, Association for a Sustainable and Smart Aquaculture | Portugal | 7 days ago
has been opened for a Research Grant (Master's) in Cell Biology, Molecular Biology, Biochemistry or similar areas. The call for applications for a Research Grant is intended to support research
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models remain a limiting factor in moving to a quantitative scale. Molecular simulation has benefited from recent advances in machine learning and generative artificial intelligence to such an extent