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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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. For example, investigating costs and benefits related to the adoption of state-of-the-art technologies that permit tracking and chain of custody. The team will also explore machine learning and artificial
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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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, Raspberry Pi, or similar platforms). • Experience with computer-aided design (CAD) software and rapid prototyping techniques, including 3D printing. • Experience developing artificial intelligence and machine
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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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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