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Bio Process Development Unit (ABPDU ). In this exciting role, you will develop AI-driven predictive metabolic models of cell physiology, metabolism, and functional behavior. You will have the
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Chemistry to conduct fundamental and applied research at the intersection of machine learning and computational chemistry. In this role, you will develop physics-informed, symmetry-aware models
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at Jefferson Lab including the 2018 tritium program (E12-11-112), the 2022 XEM2 experiment, and the recently C1 approved proposal on next generation tritium SRC measurements (PR12-26-012). You will: Develop and
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, delamination, metastability effects) Identify gaps and contradictions in reported degradation knowledge and formulate hypotheses that connect findings across studies Develop data analysis procedures (Python
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collaboration with researchers from Argonne National Lab and Oak Ridge National Lab on a collaborative Department of Energy funded project “AlphaFold for Microelectronics”. The role will be to develop data
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, or retrieval context. Develop and release benchmarks that tie model performance to real biological tasks, including gene function, fitness, phenotype, and pathway completion, with defined splits, baselines, and
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biological datasets. Develop quantitatively predictive models of biological systems. Integrate multi-omics data into quantitative computational models. Apply Monte Carlo sampling approaches to quantify
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The Earth and Environmental Sciences Area at Lawrence Berkeley National Laboratory (LBNL) seeks a postdoctoral researcher to develop and curate unique and cutting-edge AI-ready data for the U.S
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with an interdisciplinary team of scientists to develop new spatially resolved resonant soft x-ray reflectometry and magnetic reflectometry capabilities at the Advanced Light Source by implementing
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develop and optimize scientific and engineering applications leveraging high-speed network capability provided by the Energy Sciences Network or run on next-generation high performance computing and data