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, computer vision in the Division of Health Data Science (HDS) at the DOS. The position is an annually renewable professional academic appointment. Duties/Responsibilities: ● Risk predictive model for clinical
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. • Experience with machine and deep learning modeling approaches and developing Bayesian models. • Multidisciplinary skills to bridge fields such as plant disease ecology, remote sensing data, and geospatial
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more years of research training ● Advanced computer modeling skills, including experience with machine learning and/or automation, and spatiotemporal modeling ● Experience piloting drones for research
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will be working on MR spectroscopy in mouse models of creatine deficiency. Responsibilities: • Assist PI with animal preparation for scanning (15%) • Assist PI with data acquisition on the 16.4T animal
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performance analysis, graph-driven deep neural networks, data-efficient machine learning, self-supervised learning, reinforcement learning, online learning, and meta-learning with applications
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appropriate models. The candidate is expected to follow best research practices including careful documentation of experiments and data, effective communication to ensure safety and reliability, and following
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methods of data analytics (e.g., statistics, stochastic analysis, Bayesian statistical analysis), physically-based hydrology and water quality models, and the use of machine learning tools for modeling flow
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the laboratory include the study of growth factor signaling in regulating growth and metastasis of breast cancer, use of animal models to study the effect of signaling inhibitors and natural killer cell based
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scales, to integrate data science, bioinformatics, computational modeling, and machine learning, and help create sustainable solutions to improve decision-making. Areas of research may include quantifying
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professional experience as a lighting designer. Computer assisted design proficiency in drafting and design software, and 3-D modeling/rendering. Demonstrated experience and effectiveness with instructional