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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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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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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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data. 2 Postdoc Subject The main goal of this postdoc is to develop open-world 3D scene understanding models through the fusion of LiDAR-based models and VLM. This goal can be achieved by solving
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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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, Environmental Science, or related disciplines Proven expertise in environmental science and data-driven research as PostDoc Experience in acquiring third party funding in the relevant fields of data science
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profile PhD in an environment-related field followed by experiences as PostDoc in related interdisciplinary research contexts, optimally with research that aimed to work on environmental evidence synthesis
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train robust machine learning (ML) algorithms without exchanging the actual data. The benefits of such a decentralized technology over personal and confidential data are multiple and already include some
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revisit discretization methodologies in view of modern requirements and computational capabilities. The candidate will focus on developing mesh generation algorithms meeting the following criteria
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training of an artificial intelligence algorithm capable of automatically segmenting the bony structures of both healthy and fractured tibial plateaus. This will serve three main purposes: 1) Enable