22 machine-learning research jobs at Lawrence Berkeley National Laboratory in United States
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Berkeley Lab’s (LBNL ) Energy Geosciences Division (EGD) has an opening for a Postdoctoral Researcher in Machine Learning for Geological Carbon Storage to join the team. In this exciting role, you
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an opening for a Computational Biology Postdoctoral Fellow in Health Sciences. In this exciting role, you will work on computational analysis, data integration and machine learning among other methods directed
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on computational analysis, data integration and machine learning among other methods directed towards innovative approaches to decode immune responses, among other biological processes. This position will contribute
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engineering, and user experience (UX). The Usable Data Systems (UDS) group within this division seeks a Computer Systems Engineer (CSE) for UX Research & Design. This role involves incorporating user needs
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Machine Learning (ML) models and Python script or equivalent, as well as perform room temperature and cryogenic temperature electrical and mechanical measurements. What You Will Do: Perform mechanical
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laboratory. Desired Qualifications: Bachelor's degree preferred or similar experience. Experience in integration of hardware to computer control systems. Experience with analysis codes such as Python
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group analyzing deep soil warming experiments across the world. In this exciting role, you will apply mechanistic and machine learning models to study observed effects of warming on soil and ecosystem
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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
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and machine learning models to study observed effects of warming on soil and ecosystem carbon cycling and the mechanisms shaping the soil response, and to estimate potential soil carbon feedbacks with
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knowledge of an experimental laboratory. Desired Qualifications: Bachelor’s degree preferred or similar experience. Rudimentary experience in integration of hardware to computer control systems. Experience