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universities, internationally recognized for high quality in research and education. As a societal institution, we shall contribute to sustainable and democratic development and be an attractive and inclusive
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faculty position at the rank of Assistant Professor in Quantum Information Theory, with an anticipated start date of August 1, 2027. We seek candidates capable of developing an innovative research program
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. The successful candidate will assist in developing novel algorithms and integrating them into robotic platforms, helping to push the boundaries of embodied intelligence in both research and practical deployment
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problems in derivatives pricing, risk management, and algorithmic trading. Through theoretical lectures, coding assignments, and case studies, students will develop the ability to select, implement, and
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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earthquakes. For fundamental reasons, estimations from seismic waves saturate for the largest events, which results in dramatic underestimation of the subsequent tsunamis. We have developed in our group a deep
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, combining plasma proteomics with artificial intelligence may enable the minimally invasive assessment of organ-specific biological age and functional decline. This project aims to develop organ-age and organ
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on the design, development, and realization of future communications technologies. You will be part of the team and contribute to ongoing developments in theory, algorithms and translation to practice in
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identity records using profile matching algorithms and manual review. Collaborate with the Identity Services team to keep the university master identity model consistent. Metadata & Taxonomy Management
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, optimal transport, set evolution and shape optimization, calculus of variations), and/or convex/non-convex optimization methods (first-order algorithms, nonlinear/non-Euclidean gradient descents