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addition to conventional software, the scope includes engineering of AI enabled systems (primarily ML and LLM), and thus MLOps (Machine Learning Operations), datacentric AI, and legal and ethical aspects of
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. Researchers with prior experience using molecular dynamics simulations and machine learning methods to study the structure and electrochemistry of disordered materials are also encouraged to apply.The project
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combine robot–plant interaction with multi-modal perception and machine learning. Current noninvasive agricultural monitoring systems rely primarily on passive sensing, which limits sensitivity to early
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management, data analysis and data visualisation of data from clinical studies and registries using Python/R, with methods such as machine learning and network meta-analysis. Eligibility The
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Information Technology Are you interested in working with probabilistic machine learning for the next generation of AI models – uncertainty-aware foundation models, generative models and world models
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Information Technology Are you interested in working with machine learning and simulation-based inference for searches for dark matter (or other “invisible” new physics signals) at the Large Hadron
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and interpreting results, and benchmarking brain-inspired architectures against conventional machine learning methods. You are also expected to actively participate in academic discussions with
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, mathematical modeling, analysis, machine learning, optimization, mathematical foundations of artificial intelligence, mathematical ecology and mathematical statistics. Qualifications To qualify as a
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involve developing and applying high-fidelity CFD models of turbulent flows in hydraulic turbines, integrating machine learning and deep reinforcement learning algorithms with numerical simulations
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, machine learning and traffic or driving simulation to study crash occurrence, injury severity and road-user behaviour. Particular attention may be given to heterogeneous effects across road users and