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(SHORES) and the Division of Engineering, New York University Abu Dhabi, seek to recruit a Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital
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Mississippi. Apply computer vision and machine learning approaches to integrate ground-based imagery, remote sensing data, and lidar data for high-resolution flood detection and mapping. Develop and calibrate
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(HR+/HER2-) and aims to develop predictive models of therapeutic response using machine learning combined with Fourier-Transform Infrared Spectroscopy (FTIR) applied to blood, saliva, and tumor tissue
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atmospheres and detectability studies Model development of 3D stellar atmospheres Applications of machine learning and AI to exoplanet data analysis Biomarkers and habitability of Earth-like planets Where
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researchers, policymakers, NGOs, companies, international organizations, and foundations committed to sustainable nitrogen management. We welcome applications from candidates with PhDs in environmental studies
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scienceEducation LevelPhD or equivalent Skills/Qualifications PhD in computer science Background in probability, Markov chains, MDPs Knowledge about reinforcement learning and planning are a plus but not necessary
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(7T fMRI, MR Spectroscopy), electrophysiology (EEG), interventional (TMS, tDCS) and neurocomputational (machine learning, reinforcement learning) approaches to understand the network dynamics
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. Applicants with experience in Bayesian modeling, spatial statistics, mathematical modeling, data integration, uncertainty quantification and/or machine learning are encouraged to apply. The postdoc will also
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-based cameras (e.g. DVXplorer), and tactile/force sensors. 3. Strong background in computer vision and deep learning, with practical implementation experience. 4. Proficiency in programming with
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-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, and uncertainty quantification. The position comes with a travel allowance and access