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behavioral analysis, and an evolutionary framework, and range from genetic or circuit dissection within a single species to comparative studies. Planned work draws on a broad experimental toolkit: genome
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concentration/functional inequalities Markov processes and stochastic analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical
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, and interpret results. There will also be the opportunity for tool development in microscopy and image analysis. In compliance with NYC’s Pay Transparency Act, the annual base salary range
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considerations. Candidates should have a Ph.D. degree and past work in applied and computational mathematics, with a solid background in machine learning and/or numerical analysis. The candidate should have an
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statistical modeling and analysis of human performance data (reaction time, workload, situation awareness), including mixed-effects/multilevel models. Programming proficiency in Python, R, and/or MATLAB
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will have a demonstrated background in electrochemical analysis, materials synthesis, and advanced characterization techniques as exemplified by a strong publication record. Previous experience on redox