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Field
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, molecular dynamics simulations using ab initio and machine-learning potentials, and the development or application of machine-learning tools for feature extraction, property prediction, and inverse molecular
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—from cold interstellar ices to the early stages of planetary systems. The successful candidate will contribute to experimental studies simulating interstellar ice environments under controlled
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instructional faculty members and 15+ physicists in research positions. The Physics Department strives for excellence in research in Nuclear and Hadronic Physics, High Energy Physics, Atomic, Molecular & Optical
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cutting-edge experimental and computational technologies. Our aim is to dissect dynamics and cellular programmes active during human blood lineage development and to decipher how haematopoietic
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/TranSIESTA), or Molecular Dynamics (including hybrid QM/MM or ML-IP simulations) - Apply for computational resources in HPC facilities when needed. - Prepare periodic reports of the results and provide
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researchers will work in a dynamic team of staff scientists at Argonne National Laboratory. Within the team we have extensive experience with large scale molecular dynamics simulations, first principles
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molecular dynamics simulations. This position emphasizes research in the modeling of complex chemical systems, where the candidate will integrate advanced simulation techniques with modern machine learning
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photo-bases. The work will focus on modeling of adiabatic and nonadiabatic photochemical processes to capture excited states dynamics using an array of ab initio molecular dynamics methods for excited
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-Integrated 3D Skin Models”, led by Prof. Jonathan Brewer (Department of Biochemistry and Molecular Biology, SDU), Dr. Mike Barnkob and Prof. Torben Barington (Department of Clinical Immunology, Odense
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plasmas, including via methods based upon Molecular Dynamics and Particle-in-Cell techniques as well as experiments. We also envision that the ideal candidates will provide support to experimentalists and