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Field
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on applications to complex, dynamic systems. Design and simulate feedback control algorithms for thermo-mechanical systems and related applications. Collaborate with faculty and student teams to support control
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of 22q11.2 and 3q29 deletion syndromes, two rare conditions that confer the highest genetic risks of schizophrenia. Key Responsibilities: Develop and implement advanced algorithms for analyzing calcium
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The Genetics Department at Stanford University is seeking a Software Developer 2 to conceptualize, develop, supervise, and implement novel computational approaches for genomics. The successful applicant must
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Machine Learning Scientist to join the Machine Learning Science team, within the Computational Science department. The ideal candidate has a strong knowledge of artificial intelligence (AI), including
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. The focus will be on developing algorithms and techniques that optimize beamforming performance to enhance signal acquisition, tracking robustness, and interference mitigation. Benefits Our commitment
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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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multidisciplinary programme aiming to improve tools in, and knowledge from, genetics, genomics, molecular and single cell biology, spatial imaging, machine learning and novel methods of data handling to study the
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to genetic, genomic, proteomic and/or metabolomic data from hospitalized children; developing predictive algorithms for in-hospital events; investigating pattens and quality of care for hospitalized children
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highly motivated researcher to develop artificial intelligence based novel algorithms and computational workflows to predict the impact of mutations on genes in the avian flu virus and the viral host which
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 2 months ago
. Duties/Responsibilities Analysis (50%) Develop machine learning algorithms to analyze ground magnetic field perturbations Analyze the results using machine learning interpretability techniques