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Monte Carlo algorithms as directed by Professor Sandeep Sharma in the Division of Chemistry and Chemical Engineering. Essential Job Duties Develop and maintain auxiliary field quantum Monte Carlo
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Consortium’s Science Ground Segment, providing algorithm and software development, participating in data quality assurance, and performing data processing. In addition, ENSCI supports the US research community
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-of-the-art algorithms for detecting and classifying safe landing zones using computer vision techniques in order to design and develop custom models for detecting landing zones in real-time based on drone
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Livingston Observatory, Caltech, and MIT. As LHO Scientist, you will have the opportunity to apply your skills to the design, development, implementation, and commissioning of technologies that increase
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minimize coupling of environmental disturbances into detector systems. Develop techniques and algorithms to characterize and remove noise sources. Study noise couplings, high-power operations, and thermal
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to advance the project. The successful candidate will have extensive experience developing models and algorithms for analysis of noisy biological data, a deep background in analysis of stochastic processes
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further requires teamwork to implement those models in tandem with hardware development. Essential Job Duties Research and evaluation of algorithms for detecting landing zones. Development of custom
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from astrometry, flat-field determinations, PSF uncertainties, and gain drifts. The applicant will also develop algorithms for quantifying best-fit flux and errors with outlier rejection in the all-sky
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research using machine learning and reinforcement learning techniques to explore mathematical problem solving. Assist in the development, testing, and refinement of algorithms for mathematical reasoning
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candidates to apply for the position of Adaptive Optics Scientist within its Optical and Infrared (OIR) Instrumentation Group. The OIR Group develops innovative visible and infrared astronomical adaptive