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with strong analytical and numerical skills, and backgrounds in physics, theoretical neuroscience, applied mathematics, computer science, engineering, or related fields. Experience in relevant research
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degree in Statistics/Economics/Applied Math/Computer Science or related fields Knowledge of regression analysis, statistical inference, familiarity with machine learning/prediction tools Experience with
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theoretical nuclear physics, condensed matter, and radiation effects studies. Group members include professional staff in electronics, mechanical engineering, cryogenic design, and machinists, as
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or microworlds, conduct laboratory studies, and construct computational cognitive models including paradigms in Cognitive Science (Instance-Based Learning models) or AI (Reinforcement Learning). The fellow will
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and observational data from space-borne missions (most notably Kepler and TESS); be proficient in computational astrophysics, astrostatistics, and/or exoplanetary science; participate in Departmental
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, you will be contacted. Required Job Qualifications A PhD in Atmospheric Science or a related field, including chemistry, physics, computer science, and engineering. Preferred Job Qualifications
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applying interpretable AI / machine learning / deep learning / information-theoretic methods and algorithms in the context of multiscale biological networks, ranging from molecules (protein chemistry) to
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to combine the practical and the theoretical, the Robotics Institute has diversified its efforts and approaches to robotics science while retaining its original goal of realizing the potential of the robotics
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development, and how key concepts are iteratively built upon over the course of the chemistry curriculum. Regardless of topic, our methods are guided by our research questions and theoretical perspectives and
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development, and how key concepts are iteratively built upon over the course of the chemistry curriculum. Regardless of topic, our methods are guided by our research questions and theoretical perspectives and