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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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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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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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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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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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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
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software (e.g., Quantum ESPRESSO). Molecular dynamics packages (e.g., LAMMPS). Machine learning interatomic potentials (e.g., GAP, MACE, NequIP ). Machine learning libraries and frameworks such as Scikit
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or applying artificial intelligence methods to problems in the physical sciences Prior experience or coursework in machine learning, density functional theory, or molecular dynamics is advantageous Effective
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structures, understanding molecular dynamics simulations software’s such as Material Studio, LAMPPS, etc., and meticulous knowledge of analytical tools used in the pharmaceutical industry. The successful
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, which couples CPMs of cell migration and cell traction with a molecular dynamics model of ECM fiber networks, and a PDE model describing growth factors and ECM-modifying enzymes . Models will be initiated