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agents, generating knowledge on the substances currently being distributed and used. The successful candidate will contribute to the development of analytical tools and knowledge that support forensic
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deviation from the healthy distribution. But in the absence of labels, how should we direct the model to learn relevant features, and how can we determine which features are relevant? These questions
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exploited by intelligent physical systems, such as robots, vehicles, and distributed autonomous agents, to perceive and interpret their environment and support timely physical action. A central objective will
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and structure of healthy anatomy and detect any deviation from the healthy distribution. But in the absence of labels, how should we direct the model to learn relevant features, and how can we determine
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exploited by intelligent physical systems, such as robots, vehicles, and distributed autonomous agents, to perceive and interpret their environment and support timely physical action. A central objective will
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. Our mission encompasses the creation of insights that allow the development of context-aware, distributed, and embedded cyber-physical systems, with a particular focus on Internet-of-Things (IoT) and
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for 3-year PhD positions in computer science with emphasis on formal methods. Possible topics include (but are not limited to) computational complexity, distributed systems, human factors, logic
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of the position will be in developing new techniques for testing and verifying modern highly concurrent systems, such as weak-memory architectures and highly-distributed databases. The position is also open, to
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of quantum information – e.g. the no-cloning principle – to provide improved privacy in distributed computation settings. As part of the position, you will be expected to collaborate with other researchers
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. Experience with development of Triton or CUDA kernels. Experience with distributed training and LLM infrastructure (e.g. Ray, TRL, vLLM, Triton, DeepSpeed, Unsloth, VeRL) and MLOps tooling (MLflow, Weights