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Computational/theoretical chemistry and/or physics, chemical engineering, materials or a closely related field completed within the last 5 years. Preferred Qualifications: Experience with coding, electronic
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visualization; applicants are encouraged to include links to relevant code or repositories in their CV experience working with multimodal or data‑driven research workflows and translating quantitative outputs
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., specific code you wrote, modules you debugged, or workflows you designed). Highlight Transferable Skills: If your background is in a specific science domain (e.g., Physics, Biology), frame your experience in
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Max Planck Institute for Security and Privacy, Bochum | Bochum, Nordrhein Westfalen | Germany | about 1 month ago
Job Code: history of cryptography Job Offer from February 05, 2026 The Max Planck Institute for Security and Privacy (MPI-SP) in Bochum, Germany, is offering a position for one Postdoctoral Scholar
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to Inclusivity PULSE follows the European Charter for Researchers and the Code of Conduct for Recruitment of Researchers, ensuring an open, fair, and transparent selection process. We encourage applications from
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. Maintain reproducible code and documentation. Conduct simulation experiments using driving simulators or other software tools to test and validate algorithms. Perform physical experiments on robotic and
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pension schemes with beneficial mortgages and good insurance schemes. Salary in position as Postdoctoral Fellow, position code 1352 in salary range NOK from 595 000 to 677 000, depending on competence and
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regulations and contract. Skill in modeling, processing, and analyzing computational results to inform accompanying experimental efforts. Skill in the use of modern collaborative coding practices Demonstrated
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Organization National Aeronautics and Space Administration (NASA) Reference Code 0044-NPP-MAR26-ABProg-Astrobio How to Apply All applications must be submitted in Zintellect Please visit the NASA
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the performance and scalability of large-scale molecular dynamics simulations (e.g. LAMMPS) using machine-learned potentials (e.g. MACE) through algorithmic improvements, code parallelization, performance analysis