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, analysis and/or scientific computation, scientific software and algorithm development, data analysis and inference, and image analysis Ability to do original and outstanding research in computational biology
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policy and statistical methods for causal inference, access to diverse national data sources, and analytic support. Based on interests identified at the time of application, the fellow will be guided
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coarse-grained models that can be analyzed and simulated. Strong applicants with backgrounds in applied and computational mathematics, biophysics, engineering, statistical inference, and related fields
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. Operating persistent or ephemeral services supporting workflows, such as databases, workflow engines, cloud-native frameworks, AI inference front ends, and REST APIs. Coordinating dynamic service deployments
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of capabilities such as AI training and inference at scale, and tight AI-simulation coupling. What is Required: PhD in Physics, Chemistry, Computational Science, Data Science, Computer Science, Applied Mathematics
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current and next-generation galaxy and time-domain surveys. You will work closely with our teams on the Rubin Observatory ‘s LSST, Euclid, ZTF, and LS4. Main responsibilities Field-level inference and
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candidates will have (i) research experience in one or more of the following areas: statistical inference, probability, and optimization, (ii) experience working on interdisciplinary teams with biologists and
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datasets to be used in the project's experiments. - Benchmarking of membership inference and model memorization techniques. - Development of gradient-based approximate machine unlearning approaches
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or ephemeral services supporting workflows, such as databases, workflow engines, cloud-native frameworks, AI inference front ends, and REST APIs. Coordinating dynamic service deployments and specifying storage
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: Development, performance analysis and optimization of end-to-end science workflows, including those originating at DOE facilities. Deployment of capabilities such as AI training and inference at scale, and