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atomistic simulations, scientific machine learning, reaction modeling, and integration of computational and experimental data. The associate will develop reproducible computational workflows, collaborate with
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geoscientific process models, as demonstrated by presentations, publications and/or repositories Expertise in applying Bayesian statistical methods, machine learning methods, or related statistical inference
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field. • Experience conducting medical imaging research. • Experience developing artificial intelligence and machine learning approaches for research. • Strong command of statistical methods and their
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reinforcement learning approaches for autonomous guidewire shaping and control. • Design experiments, validation strategies, and quantitative performance metrics for robotic navigation and wire-shaping systems
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or interest in structural biology. The Snell Laboratory collaborates closely with AI and classical machine learning developers, and the selected candidate should have expertise or an interest in acquiring