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the structure of real prospection activities by integrating multiple, heterogeneous geospatial and archaeological data layers. Within this environment, we will design and evaluate new reinforcement learning
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layers. Within this environment, we will design and evaluate new reinforcement learning algorithms capable of operating in large, partially observed spatial domains to infer efficient, interpretable
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impact on public health policy and society. The position focuses on developing and evaluating learning-based approaches for controlling infectious disease spread under realistic epidemiological
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back boundaries and set a course for the future – a future that you can help to shape. As a postdoctoral researcher, you will contribute to the implementation and scientific evaluation of direct-from
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, socioeconomic, and geospatial data; Contribute to patient-level and population-level modelling approaches, including temporal, spatial, and multimodal prediction frameworks; Apply and evaluate methods for disease
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in creating and evaluating machine learning models.•Familiarity with deep learning framework, such as PyTorch or Tensorflow.•Experience in data preparation, preferably in a bioinformatics context (data
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-year contract will be offered, with the possibility of extension following a positive evaluation and depending on project funding. Interested? Your solicitation is expected before 20 September 2026
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activities focus on topics such as: AI agents and computer-use agents Security, robustness, and trustworthiness of AI systems Evaluation, benchmarking, and testing of AI systems Runtime monitoring and
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investigate end-to end guarantees of correctness. When successful, this will have a major impact on the way combinatorial optimization software is developed, evaluated, and used: the proofs produced
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school children (Grades 4–6) and whether this skill can be effectively trained at this age. LifeCraft was adapted for this younger population and has already been evaluated in an initial trial. Findings