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Field
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verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research independence, the capacity to support junior team members, and strong communication
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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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of political science and be proficient in conducting quantitative analyses. Experience with large language models, machine learning, and/or programming in R or equivalent programs is an advantage but not a
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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to the project’s scope, such as mechanistic interpretability of LLMs, robustness verification of machine learning models, and conformal inference. Applicants should demonstrate scientific creativity, research
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The National Energy Research Scientific Computing Center (NERSC ) at Berkeley Lab seeks a highly motivated Postdoctoral Researcher — Scientific Machine Learning (NESAP) to join the Workflow
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models, machine learning, and/or programming in R or equivalent programs is an advantage but not a requirement. Alongside developing own research ideas, applicants should be capable of turning those ideas
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, inverse problem, machine learning Expertise in the analysis of a wide range of geospatial and geodetic data sets Strong expertise in programming with Python or MATLAB As position requires stakeholder
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to translate ground-breaking technologies into commercial products that solve big problems. We support research that universities, companies, and venture capital firms don’t fund because they view it as too
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training deep learning models on biological, chemical or related datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep