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available in the Geometric Machine Learning Group at Harvard University, led by Prof. Melanie Weber. This role offers an opportunity to perform research on Riemannian Optimization. The ideal candidate has a
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with colleagues from a wide range of backgrounds and areas of expertise Most candidates will have experience with data science or machine learning, but ultimately, we’re more interested in how you think
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uses. To investigate feature-level just-noticeable difference modelling for machines to facilitate assessment and optimization. To formulate a comprehensive visual feature codec for machine uses
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provide a creative, nurturing campus environment where our students can become the best individuals possible, learn from the best and brightest faculty, and make a positive difference in the community
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in Artificial Intelligence (Machine Learning and Statistics) at CentraleSupélec, · Joël Eymery, Head of the Nanostructures and Synchrotron Radiation Team at CEA Grenoble, · Jean-Sébastien
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comes with no teaching obligations BASIC RESPONSIBILITIES AND OBLIGATIONS Conducting research related to the scientific project titled “Calculus of variations in machine learning problems”, in particular
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experiments, including neural responses from visual and auditory cortices recorded over multiple days Apply and adapt advanced machine learning frameworks (SPARKS and CEBRA) for supervised and unsupervised
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Assistant will complete CITI Training and other Computer Based Learning Trainings to gain access to necessary study platforms such as Institutional Review Board (IRB), Integrated Healthcare Information System
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-looking vision for a 21st-century education, and a deep commitment to promoting inclusion, we open a world of opportunity to students and empower them to make a difference around the world. Through learning
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Engineering, Physics, or a similar field. Preferred Qualifications Strong technical background in one or more of the following areas: signal processing, advanced data analysis, statistics, and machine learning