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Field
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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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| Computer Science and Engineering We are seeking a postdoctoral researcher to work on problems at the intersection of algebraic coding theory, matroid theory, and pseudorandomness/derandomization. The focus
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The Applied Mathematics and Plasma Physics (T-5) group within T-Division conducts research across a wide variety of topics including fusion science, space plasmas, numerical analysis, computational imaging
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Ph.D. by the time of appointment in statistics, mathematics, computer science, engineering or a related discipline Required Other None Additional Preferred Experience working in one of the following
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, statistics, applied mathematics, bioinformatics, computer science or a related field and 2) completed postdoctoral training prior. To be considered, those interested must apply through The University
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: Experimental condensed matter physics, cosmology and astrophysics, particle astrophysics and dark matter, high energy, atomic, pulsar and biophysics; theoretical cosmology, condensed matter, mathematical, and
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research program sponsored by federal agencies such as the U.S. Department of Defense and the National Science Foundation (NSF). The focus of the research is on developing innovative physics-informed deep
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Science, Statistics, Mathematics, Computer Science, or a closely related field. Demonstrated expertise working with multivariate time-series and spatiotemporal datasets. Advanced proficiency in Python
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, Robotics, Computer Vision, or related disciplines. Proven expertise and hands-on experience in one or more of the following areas: large language models (LLMs), end-to-end learning, AV localization
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The successful candidate will have earned a doctoral degree (Ph.D. or Psy.D.) in Clinical or Counseling Psychology from an APA-accredited program and will have completed an APA-accredited doctoral internship