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, time-limited, 12-month, non-tenure track Research Assistant Scientist in the area of Applied Category Theory. The initial appointment is for one year, with reappointment anticipated based on funding and
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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expertise in wireless communications, communication theory, information theory, applied probability, and optimization • Excellent written and verbal communication skills Preferred Qualifications • Prior
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change theory and practice, implementation science, and associated measurement and analytic techniques. The candidate is expected to help bridge these domains using validated statistical tools. Applicants
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, econometrics or another relevant field. You will have substantial relevant research experience in complex trait genetics, a proven ability to code and a strong publication record. This is a full time, fixed term
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contributing positively to a collaborative research environment. Desirable: experience with building energy or power system applications, cooperative or coalitional game theory, or high-performance computing
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systems theory Excellent analytical and problem-solving skills Desirable criteria Advanced programming and data analysis skills Computational neuroscience background Behavioral data analysis skills Strong
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University explores synergies between nonlinear control theory and physics informed machine learning to provide formal guarantees on performance, safety, and robustness of robotic and learning-enabled systems
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Organization National Aeronautics and Space Administration (NASA) Reference Code 0329-NPP-NOV25-JPL-EarthSci How to Apply All applications must be submitted in Zintellect Please visit the NASA
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studies and interactive AI systems. This position will be funded based on an initial 2-year contract + 2 years extension. The key idea is to apply theories, models, and methods from psychology to improve