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suitable data models [CSC+23]. Objectives As far as the design of efficient numerical algorithms in an off-the-grid setting is concerned, the problem is challenging, since the optimization is defined in
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., cores, memory hierarchy, accelerators, interconnects) and software-level decisions (e.g., task mapping, scheduling, compiler optimizations), which together lead to a combinatorial explosion of possible
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, including image acquisition, processing, analysis, and interpretation Develop and validate new imaging techniques, algorithms, or software to improve diagnostic accuracy and patient outcomes Collaborate with
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expertise [2]. Unlike scheduled orthopedic procedures, trauma surgery has seen little integration of artificial intelligence in preoperative planning. Currently, identifying bone fragments and determining
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minimizing error and maximizing efficiency, is computationally challenging—no known polynomial-time algorithm exists to solve it optimally in all cases. Because of this complexity, researchers typically rely
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openness to interdisciplinary collaborations Expertise in some area of computer science such as computational complexity, algorithms, data structures, logic in computer science and AI, semantics, theory
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techniques and the structure of bilevel problems in large-scale settings. Objectives The goal of this postdoctoral project is to develop scalable blackbox optimization algorithms tailored to bilevel problems
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on stochastic Riemannian optimization algorithms, these methods still suffer from limitations in computational complexity. The post-doctoral fellow will build upon this preliminary work to investigate
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algorithms for dynamic structured data, with a particular focus on time sequences of graphs, graph signals, and time sequences on groups and manifolds. Special emphasis will be placed on non-parametric
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robots, avatars, social bots, virtual assistants, or AI-driven systems such as chatbots, recommender algorithms, or generative AI. The position focuses on how communicative processes are shaped by and