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Field
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using molecular dynamics methods Additional Information Benefits * employment at one of Poland's leading technical universities, which co-creates modern science and education * a friendly working
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by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Job description The AICell Lab (https://aicell.io ) in the department of Applied Physics
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SUSMAT-RC - Postdoc Position in Computer-Aided Design and Discovery of Sustainable Polymer Materials
, including molecular dynamics, quantum mechanical simulations, and machine learning. Proficiency in programming languages and computational software’s. Strong motivation and passion for research in the field
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Posting Summary Logo Posting Number RTF00185PO26 USC Market Title Post Doctoral Fellow Link to USC Market Title https://uscjobs.sc.edu/titles/156387 Business Title (Internal Title) Post Doctoral
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on endocrine and neuroendocrine tumours, integrating preclinical models, molecular profiling and translational approaches to identify novel therapeutic vulnerabilities and biomarkers associated with treatment
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a Postdoctoral Research Associate to train in interdisciplinary projects involving developing new AI-driven Molecular Dynamics (MD) simulation methods and apply them to drug discovery in multiple
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, medicine, nursing, radiology and midwifery. The department's research combines basic and translational, molecular and computational approaches, with a particular focus on cancer biology, while also
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, medicine, nursing, radiology and midwifery. The department's research combines basic and translational, molecular and computational approaches, with a particular focus on cancer biology, while also
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, biophysics, spectroscopy or microscopy, systems biology, theoretical and evolutionary biology, community ecology, ecological modeling, molecular biology, biochemistry, dynamical systems, and sports medicine
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, or foundation models. Familiarity with atomistic simulations (e.g., density functional theory, molecular dynamics). Interest in developing broadly applicable machine-learning methods for physical sciences