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fundamental research in physics-informed and symmetry-aware machine learning for nonadiabatic excited-state molecular dynamics. Develop and evaluate equivariant graph neural networks and related architectures
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epigenomic data analysis or gene regulatory network inference Ability to work independently and collaboratively in a multidisciplinary environment Excellent written and spoken English skills What we offer
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potential impact across quantum networking, communications, and computing. Research Focus Design and fabricate superconducting devices that leverage the nonlinear kinetic inductance of thin superconducting
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(particularly computer vision), (2) machine learning for data analysis, (3) sensor technologies (e.g., electromagnetic sensors), (4) design and integration of mechanical/electrical devices; and an interest in
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regulatory networks in health and disease (m/f/d) Stellenanzeige merken Stellenanzeige teilen We are looking for an enthusiastic Postdoc/PhD to join our research group to study gene regulation in
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combining experimental biology and computational genomics. Main duties and responsibilities Working and collaborating on research projects Analysis and publication of results Build a strong network in the
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analysis? Are you inspired by applications to engineering, physics, or industrial problems? Do you thrive in a multi-disciplinary environment? Information Many of the most challenging problems in science and
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collaboration with faculty and research staff at NCEMS, will conduct original research involving proteomics, mass spectrometry data analysis, protein interactions, protein complexes, post-translational
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We are seeking a postdoctoral researcher to support the project "Social networking in the era of artificially intelligent agents” funded by the Research Council of Finland. The direction is to
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Postdoc Position for Computational Genomics, AI Precision Oncology, Cancer Biology and Immunobiology
University of Pittsburgh, Pittsburgh , Pennsylvania, US | Pittsburgh, Pennsylvania | United States | about 2 months agoinsights through iterative, hypothesis-driven computational analysis. 2) Characterizing the landscape of structural mutations—including intragenic rearrangements (IGRs)—across cancer types and modeling