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Field
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solutions to compelling problems in energy and security. The Discrete Algorithms Group at Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher for a two-year position specializing in
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learning algorithms. We combine statistical methods with online reinforcement learning algorithms to develop reinforcement learning algorithms and inferential tools. The successful applicant will be expected
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typologically diverse languages Creating self-supervised learning algorithms that can assess phonological development and speech complexity in children from birth through age 6, with applications to both typical
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optimization under uncertainty, constrained query evaluation, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms
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The Position We are seeking an independent and motivated researcher for a Postdoctoral Fellowship in Aviv Regev’s lab to apply develop edge algorithms for analysis of single cell genomics profiles
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methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data modalities. The candidate will have the opportunity to work
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work environment. We are seeking a part-time Research Assistant in computer vision and machine learning for human behavior analysis and modeling. The successful candidate will investigate new algorithms
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strong overlap in the fields of topological quantum field theory, quantum condensed matter theory, and quantum computing and algorithms. Qualifications Applicants must hold a PhD in physics or a related
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high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms. No prior biomedical training is required—just strong quantitative
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machine learning. Essential Duties and Responsibilities: Develop and implement advanced reconstruction algorithms for correlated and low-dose imaging modalities. Maintain and extend Python-based software