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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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, 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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, Computer Science or related fields (for PhD); Doctorate in Physics, Computer Science or related fields (for Post-Docs). The positions are funded via the Cluster of Excellence (Machine Learning for Science), the ERC
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information systems engineering. The group conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities and Machine Learning/AI on organisations from both
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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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through soft, disordered materials, including auto-regulated networks, composite soft solids, and exotic photonic biomaterials. The lab has two fully funded PhD and/or postdoctoral positions available
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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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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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from varied sources, and machine learning methodologies Required Application Materials: 1. A cover letter describing: a. Your interest in this position b. Your relevant training and experience c. Your
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have experience in code development and/or use of first principles methods (e.g. DFT) and/or machine learning methods, as well as experience in working with experiments and/or experimental collaborators