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Field
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binding pockets. About the role We are seeking a highly motivated researcher to develop artificial intelligence based novel algorithms and computational workflows to identify domain functional families
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; collaborates with principal investigator (PI) to develop and implement goals and objectives of studies; develops algorithms for medical image reconstruction, including but not limited to computed tomography
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designs and methods, clinical trial methods, Bayesian methods, and developing R packages and scalable algorithms. Opportunities for collaboration across the Department of Biostatistics and the Medical
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 1 month ago
. Duties/Responsibilities Analysis (50%) Develop machine learning algorithms to analyze ground magnetic field perturbations Analyze the results using machine learning interpretability techniques
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-informed machine learning (PIML) models for the prediction of physical and chemical properties using data from experiments and computation constrained by physics requirements. § Implementing algorithms
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undergraduate students during summer months. Apply existing remote sensing based algorithms, such as 3T, to estimate latent heat fluxes from thermal imagery and soil water balance. Develop and test upscaling
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supervision conferring with higher levels only in unusual situations. 8. Performs other job-related duties as required. Responsibilities: -Develop and implement advanced numerical algorithms and deep learning
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and analysis Experience with movement analysis and signal processing (especially as applied to locomotion/ gait) Expertise in developing novel algorithms, but also understanding, optimising and applying
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collaboration in optimising data analysis algorithms for the raw detector data, including improving position reconstruction and pulse-shape discrimination algorithms which can be implemented on in the front-end
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algorithms in extremely complex and enormously large spaces motivated by physics and chemistry Developing interpretable AI for scientific discovery in physics (example here ) Formal mathematics (using Lean’s