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, regardless of gender, functional ability and cultural background, or whether you have been out of work for a period of time. Salary and conditions As a Postdoctoral Fellow (code 1352) you are normally paid
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widely used by the academic community (about one thousand citations per year), and has been a flagship code of the MaX European Centre of Excellence for exascale computing in Materials Science (www.max
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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 Extraterrestrial Physics, Garching | Garching an der Alz, Bayern | Germany | about 2 months ago
Job Code: 04/2026 Job Offer from February 02, 2026 Astrochemistry Postdoctoral positions on Computational Chemistry (m/f/d) Job ID: 04/2026 The Max Planck Institute for Extraterrestrial Physics (MPE
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able to conduct a range of bioinformatic approaches involving the use of code to conduct complex comparative genomics, implement HMM searching strategies and conduct phylogenetic analysis on a grand
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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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. The university is an equal opportunity employer, including veterans and disability. As required by Ohio Revised Code section 3345.0216, Ohio State will: educate students by means of free, open and rigorous
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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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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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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