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foundational and applied topics in computer vision and machine learning, with particular strengths in inverse problems, generative models, and geometric deep learning. We work across diverse application areas
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magic, quantum circuits, quantum algorithms (3) Quantum many body computations, including sign problem theory, Quantum Monte Carlo method, DMRG, and machine learning Appointments of postdoctoral
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, computer vision, machine learning, and/or related Programming and technical skills, including GitHub profile (if existing) List of two referees (including contact details) and (if available) their support
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Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in
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-electron Schrödinger equation for fermions and bosons with high accuracy and on the application of these methods to problems in the physics of oxides, semiconductors and their surfaces. Machine learning
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is currently building and commissioning a network of calibrated multi-sensor observatory-class systems, and developing novel machine learning methods with the aim of collecting science-quality data
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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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organizes social and career development events. List of projects: The candidate is encouraged to consider one of the project ideas listed. The candidate’s merits and motivation for this project idea
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the intersection of machine learning and genomics. The project involves the development and application of advanced machine learning and deep learning techniques to understand the sequence-function relationships
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Model of Immersive Learning (CAMIL) by empirically examining how immersive VR supports learning processes across educational contexts. This position is part of a larger project funded by the Nationaal