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Field
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of interest to BNL and the Department of Energy (DOE). Topics of particular interest include: (i) development of novel machine learning models and adaptation of existing approaches for scientific applications
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Assistant professor (post-doc) at the Laboratory of Nuclear Proteins at the Faculty of Biotechnology
of muscular dystrophy using iPSCs-based in vitro model, transcriptomics and biochemistry and molecular biology methods. www.ncn.gov.pl/sites/default/files/listy-rankingowe/2021-09-15okipi34a/streszczenia/536263
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at the Department of Cell and Molecular Biology. The Elf group works across traditional disciplinary boundaries to explore life at the molecular level. We build physical models of key biological processes and develop
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degradation, and connect them to cell- and pack-level safety performance. · Design model-based and data-driven frameworks for battery management systems, including health monitoring, early fault detection, and
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Is the Job related to staff position within a Research Infrastructure? No Offer Description Gammapy is an open Python library for astrophysical very-high-energy (VHE) gamma-ray data analysis. Amongst
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The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 20 days ago
publication record in biomolecular modeling and computational biophysics/chemistry. Programming skills (e.g., bash scripting, python, Fortran, C++ and CUDA), expertise in computational modeling (such as
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. Advanced cell and mitochondrial biology: primary human fibroblasts, mitochondrial bioenergetics, import assays, respiratory chain phenotyping, in vivo disease models. Multi‑omics: single‑cell and bulk
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, you will work with advanced 3D in vitro models of fibrosis and metabolic disease, using multiple primary and immortalized human cell lines. Your research will focus on identifying molecular mechanisms
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related to the exchange of momentum, energy, and mass. Significant challenges persist in understanding processes and feedbacks. The Land-Atmosphere Feedback Initiative (LAFI, https://lafi-dfg.de ) is an
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a machine learning model (foundational model) to propose protocols of sequential induction of transcription factors to generate desired cell subtypes. The project will be conducted in close