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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
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, cryo-electron microscopy (cryo-EM), cryo-EM-based polyclonal serology (cryo-EMPEM), molecular dynamics simulations, machine learning, and structural biology to define epitopes and engineer improved
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initial contract of two years Background The post-doctoral researcher/university researcher will be working on the EU’s Destination Earth Climate Change Adaptation Digital Twin Project, Phase 3 (https
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labs across HHMI. This role is part of the AI+CryoET project within AI@HHMI, a multi-institutional project at the intersection of cryo-electron tomography (cryoET), molecular dynamics simulation, and
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KTH Royal Institute of Technology, School of Engineering Sciences Job description The AICell Lab (https://aicell.io ) in the department of Applied Physics at KTH and Science for Life Laboratory is a
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leverages periods of intense (tropical) fieldwork with molecular laboratory work and arthropod husbandry. The successful applicant will be hosted at the Wybouw research group, Department of Biology, Ghent
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or more of DFT, molecular dynamics, multiscale/materials modelling, or machine learning for the sciences including, Bayesian methods, uncertainty quantification, scientific AI workflows, automated discovery
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studies on the oxetane-cyclodextrin host-guest compounds based on PEDOT:PSS-based UV-absorber electrodes. The goal is to investigate the dynamics of photon-generated excited states and molecular
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to the project mentioned above using standard and enhanced sampling molecular dynamics simulations, and machine learning approaches. Participate in manuscript preparation and dissemination of research findings
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and software development. Proficiency in English (written and spoken). (Preferred) Experience with any of the following: Electronic structure software (e.g., Quantum ESPRESSO). Molecular dynamics