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Field
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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the ability to work together with colleagues and teach and mentor students from diverse backgrounds and perspectives. To apply, candidates should submit a cover letter, curriculum vitae, and contact information
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such as data science, AI, computer science, machine learning, Earth system science, climate etc., with a thesis subject relevant to the description of the tasks outlined above. Additional requirements In
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We are seeking a Postdoctoral Researcher in Human-AI interaction to join a research group focused on studying learning and decision-making in humans and machine learning systems led by Prof Chris
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, evolutionary biology, computer science, physics, applied mathematics, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control
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conduct policies about other schools at Duke University. Compliance with all applicable University and departmental policies and procedures. The postdoc candidate is expected to: 1)Development of machine
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the possibility of yearly renewal subject to funding availability. Responsibilities • Design and implement deep learning architectures, AI agent pipelines, and computer vision algorithms to achieve project goals
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decays, searches for supersymmetry and other new phenomena, and measurements of rare standard model processes. We vigorously pursue the use of machine learning techniques for data analysis. Candidates must
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About the Opportunity This job seeks a postdoc to work on one or more projects related to the development and assessment of wearable robotics. Possible projects topics include: Developing machine
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data management sufficient to create, transform and integrate data in a variety of resolutions and formats. Analysis will include running machine learning algorithms (e.g., Random Forest, CART) and