27 buildling-information-modelling Fellowship positions at Lawrence Berkeley National Laboratory
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technologies for accelerated discovery in biological ecosystems. What You Will Do: Develop, test, benchmark, and tune AI and foundation models with experimental biochemical data. Create computational biochemical
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investigating host-pathogen interactions, inflammation, and immunity in cell co-cultures and animal models, and potentially, clinical information. You will integrate systems biology, multi-omics, and discovery
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analysis on data derived from experimental models such as cell culture systems, three-dimensional tissue models, and in vivo animal models, and accrued clinical data, metadata and others. Develop machine
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reports. What is Required: Ph.D. in Mechanical or Chemical Engineering or related field. Some programming experience (R or Python). Experience constructing and evaluating process models in software such as
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experience. Background in applied/numerical mathematics. Experience with the C++ programming language. Experience using GPUs. Experience modeling nonlinear optics. Notes: This is a full-time, 2-year
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to mitigate any observed bottlenecks by means of materials design or device straggles. Such analyses may include modeling of the polymer properties and various physics occurring in these material systems. You
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these processes in models, and how to use the models to extrapolate in space and time. Although the position is based out of Berkeley Lab, the successful candidate will also work closely with researchers
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models. You will integrate systems biology, multi-omics and discovery approaches with mechanistic information to ultimately address key questions concerning determinants of innate immunity. You will
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of the group. What You Will Do: Use simulations to model and predict detector behavior, contributing to the optimization of detector design. Collaborate with technical and research colleagues to develop
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electron tomography data. Develop new data-driven methods that leverage physics-informed machine learning for reconstructing non-rigid deformations and generative modeling of conformational heterogeneity in