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within the field of machine learning, search, and reasoning techniques. Demonstrable knowledge and/or experience in algorithms and programming is a must; Is able to translate and convert this knowledge
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this postdoc position, the focus is on methods and algorithms for large-scale graph analytics, in particular network science approaches for analyzing longitudinal, population-scale relational data derived from
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algorithms to shape the liveable cities of tomorrow? Job description Human-centred AI techniques, such as Reinforcement Learning from Human Feedback (RLHF), hold great potential for supporting design
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competence in three-dimensional Lagrangian Particle Tracking, Object-Aware Shake-the-Box algorithms and helium-filled soap bubbles seeding for large-scale 3D-LPT experiments. You are familiar with LaVision
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probabilistic generative models for networks; analyze real network data from different application domains; design efficient algorithmic implementations of the theoretical models. You will be supervised by Dr
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software and demonstrators. Academic skills to formulate novel architectural contributions integrating cognitive robotics algorithms into systemic solutions and publish them. Proven ability to finish
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setting, enabling fast and predictable adaptation with minimal overhead. A central focus is the co-design of algorithms with edge hardware and embedded platforms. You will investigate implementation
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linearization with limited or imperfect models. Learning-enabled control dynamics Embedding optimization and learning algorithms (e.g., SGD, Bayesian updates) into control design and analysis. Attack-tolerant
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PROTEUS. Ultimately, the causal reasoning and uncertainty algorithms you build will serve as the quantitative engine for the "Copernicus Agent," an AI assistant designed to give European policymakers
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, Engineering, mathematics or related disciplines with a strong background in data analysis, mathematical modeling and algorithms Good programming skills in Python/C/C++ Good oral and written skills in English