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position within a Research Infrastructure? No Offer Description The Machine Learning for Health team at the Institute for Molecular Medicine Finland (FIMM) , University of Helsinki, is currently seeking a
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electronic excited states. Further, we use and develop classical and quantum dynamical methods to explain molecular photochemical process, also in complex environments. Your personal sphere of play: A
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The Machine Learning for Health team at the Institute for Molecular Medicine Finland (FIMM) , University of Helsinki, is currently seeking a highly-motivated postdoctoral researcher to join our team
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application consists of: An application Transcript(s) – For this opportunity, an unofficial transcript or copy of the student academic records printed by the applicant or by academic advisors from internal
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WPI-AIMEC established by Tohoku University and JAMSTEC | JP Nagar III Phase, Karnataka | India | 2 months ago
predictive simulation), and related fields. Cluster 2 : Coastal Marine Ecosystem Change Cluster 2 aims to advance mechanistic understanding of coastal ocean-ecosystem dynamics through cross-disciplinary
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at least one of the following areas: excited-state simulation methods, multiscale simulations (QM/MM a plus), catalysis (biological, homogeneous, or heterogeneous), molecular dynamics simulations, sampling
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computational chemistry techniques, including molecular dynamics, quantum mechanical simulations, and machine learning. Proficiency in programming languages and computational software’s. Strong motivation and
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application consists of: An application Transcript(s) – For this opportunity, an unofficial transcript or copy of the student academic records printed by the applicant or by academic advisors from internal
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of high-throughput imaging and molecular data (i.e., genome, transcriptome, epigenome, and more). The methods would be able to systematically integrate biomedical/biological knowledge to improve
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Science for Life Laboratory pursues interdisciplinary research in cell and molecular biology. The group is led by Wei Ouyang and combines wet-lab work with automated experimental platforms and AI-driven