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Field
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are highly desirable. Familiarity with data science and machine learning applications for analytical chemistry, industrial/agricultural facilities, and techno-economic or life-cycle analysis is considered a
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projects and deliverables May supervise undergraduate students working on the AI/ML projects QUALIFICATIONS PhD (or equivalent) in Machine Learning, Computer Science/Engineering, Biomedical Engineering, or
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mathematical structures and computational algorithms underlying modern machine learning and artificial intelligence. Relevant themes include geometric and algebraic methods for learning, structure-preserving
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streams with perturbation signatures and fit these. For these fits, we will explore the speed up from using GPUs as well as machine learning techniques, e.g. simulation-based inference. Finally, we will use
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in a Unix environment and on high-performance computing equipment. Learning Objectives: Under the guidance of a mentor, you will have the opportunity to learn to: (a) apply methods in computational
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. Candidates should be able to rapidly learn new approaches, work independently, generate publication quality data, and craft the data into manuscripts and fellowship applications. Job Description: Key Roles and
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duties as assigned. REQUIREMENTS: REQUIRED: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong programming skills. Strong background in machine
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equipment or related technologies artificial intelligence, machine learning, image analytics, computer vision or related computational approaches applied to healthcare research (desirable) a peer-reviewed
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are nurturing an environment for high-impact research and innovation in computer and data science, digital engineering, the advanced life sciences and medicine, and other tech fields. Partnership is what sets our
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analysis, ideally using Python-based tools and frameworks, with familiarity in areas such as statistical modelling, machine learning, computer vision, or optimisation approaches for experimental systems