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. Documented research experience in modern deep learning (e.g. generative models, Bayesian deep learning or large pre-trained models) and excellent programming skills in Python and a modern deep learning
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earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc. Documented experience in machine learning, in particular deep generative
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, such as building web interface, web mapping, spatial database, and web services. Experience and expertise in graph-based deep learning for intelligent transportation infrastructure and systems
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-facing web GIS dashboard. Investigate the forest, landscape, and climate conditions that drive storm susceptibility, using major windstorms as natural experiments and interpretable machine-/deep-learning
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years prior to the application deadline. Experience with machine learning for scientific applications. Experience with deep learning frameworks such as PyTorch or TensorFlow. Experience with atomistic
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leverage reinforcement learning, deep learning, and generative AI, and evaluate against the research front in mathematical optimization strategies, to enable efficient, robust, and adaptive evacuation
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modern deep learning frameworks (PyTorch, JAX, or equivalent). Have good software engineering habits — modular, well-documented, reproducible code. Are comfortable working in interdisciplinary teams and
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raises deep and largely unsolved challenges. As a postdoctoral researcher, you will tackle exactly this question: how to generate code with AI and formally verify that it does what it should. Your work
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measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with modern deep learning frameworks (PyTorch, JAX, or equivalent). Have
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description Work on EU projects to develop next‑generation transport, emission and health forecasting models by integrating deep learning, xAI, and diverse data sources such as traffic sensors, smart‑card data