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guidance, navigation, and control (GNC) systems. The successful candidate will develop and validate Bayesian and non-Gaussian estimation algorithms, data assimilation methods, and tracking frameworks
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on cognitive diagnostic assessments and student learning in introductory STEM courses, with a focus on Bayesian quantitative methods and mixed-methods education research. Position Number 4C1852 Department
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: Experience with novel or high-throughput characterization methods; accelerated stress testing and stability evaluation of thin-film photovoltaics; and data-driven experimental optimization, including Bayesian
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. Experience with Bayesian methods, graph/network analytics, reinforcement learning, or other advanced AI approaches relevant to industrial systems. Experience with geospatial analysis, spatial data integration
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with advanced statistical techniques (optimal Bayesian, Markov Chain-Monte Carlo, etc.) to solve the forward and inverse problems involved. Additional information about AGAGE, CS3, and MIT atmospheric chemistry
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experience in one or more of: large-scale data analysis, time-series photometry, spectroscopy, astrometry, Bayesian/statistical inference, and/or software development for astronomical datasets. Department
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. Proficiency in Python, MATLAB, or R. Strong quantitative and analytic skills. Preferred Qualifications Experience with evidence-accumulation models (DDM, sequential sampling, Bayesian models). Experience with
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designing/programming experiments, recruiting/running participants, developing and using computational modeling approaches (Bayesian, RL, neural networks) to analyze behavioral and neuroimaging data
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relevant to modern data science (e.g., Bayesian or frequentist inference, information theory, uncertainty quantification, high-dimensional methods). Programming skills in Python and/or R, with evidence of
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areas Biomedical applications, social determinants of health or other demographic health areas Spatial microsimulation, spatially weighted regression, combinatorial optimization or Bayesian network