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The Nuclear Science Division at Lawrence Berkeley National Laboratory is seeking a Postdoctoral Fellow to work on the development of an inclusive electron-nucleus scattering cross section model
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biological questions. Opportunities to Contribute Conduct research in the area of statistical modeling for single-cell multi-omic data. Perform clustering, differential expression analysis, predictive modeling
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including causal inference and prognostic/risk prediction modelling. It will be essential to have evidence of strong data management skills and experience managing large linked administrative datasets using
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. Opportunities to Contribute Develops dynamic river‑lake network models to assess geochemical and ecological changes—including carbon cycling and elemental fluxes—resulting from enhanced rock weathering. Writes
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initial problem specification, data gathering and analysis, model creation, and implementation Strong understanding of artificial intelligence applications and methodologies, such as working knowledge
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the laboratory's focus on neuroimmune interactions and trauma-related physiology. Develop and implement mouse models relevant to neuroimmunology, stress biology, cardiovascular physiology, and immune-mediated
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-based perturbations, base/prime editing, and targeted epigenetic modulation). Functional genomics in neuronal systems, including transcriptomic and epigenomic profiling. Model systems such as human iPSC
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development of measurement, evaluation, and analytic strategies to inform policy and systems transformation – 45% Develop conceptual frameworks, logic models, measurement plans, and analytic strategies, drawing
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water quality, development of synthetic stormwater recipes through in-depth literature review and data mining, monitoring and modeling of stormwater treatment systems, investigation of maintenance and
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experiments, and connect them into live experimental campaigns so that AI checks every candidate before it reaches the robot deployed on the Genesis American Science Cloud with the Materials Project as