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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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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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of natural language processing, machine learning, artificial intelligence, and human-computer interaction. Established within the School of Computer Science, LTI pioneers innovative approaches to understanding
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You have academic qualifications at PhD level, for example within the areas of bioinformatics, machine learning or forensic odontology. We favour experience in computational data analysis, and the
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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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Location: Ithaca, New York 14853, United States of America [map ] Subject Areas: Veterinary Medicine / Bacteriology Veterinary Pathology Appl Deadline: 2025/09/30 11:59PM (posted 2025/08/01, listed until
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-dimensional probability, concentration and functional inequalities ? Mathematical aspects of machine learning and deep neural networks ? Free Probability aspects of Quantum Information Theory. While excellent
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periods of time, repetitive motion related to computer work. Shift Monday-Friday 8:00am-5:00pm, weekends or evenings may be required by project work. This position is eligible for complete remote work. Job
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of the project is to exploit such data to develop generative models for aptamer design. The candidate is expected to have a strong background in machine learning and statistical physics, with a real interest for
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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