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develop a new generation of hybrid models combining large-scale machine learning with physical knowledge to represent interactions between mobile robots and their environment. The research will address
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, statistics and scientific computing. * Proficiency in a scientific programming language, particularly Python, and the ability to develop reproducible processing procedures. * Knowledge of machine learning and
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learning, epigenomic data, and mechanistic modelling. The mission is to contribute to the development of predictive models of the replication initiation probability landscape (IPLS) from limited experimental
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investigate out-of-equilibrium dynamics in high-dimensional disordered systems (including models relevant to machine learning and optimization) by characterizing the fixed points (metastable states, attractors
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astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, in particular simulation-based
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decision systems for pricing and market strategies. In particular, algorithmic repricing tools allow real-time price adjustments based on predefined rules or machine-learning techniques. While recent
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predictive models of immune responses. Develop advanced and innovative machine learning methodologies and analyze data. The postdoctoral researcher will join the groups of T. Mora and A. Walczak, whose
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consensus. The successful candidate will develop the algorithmic foundations of this framework—making the motion-generation stack modular and heterogeneous (combining model-based control with learning-based
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different dyadic motor coordination tasks. A range of neurophysiological measures (EEG, ECG and fNIRS) as well as behavioural measures will be recorded simultaneously from both partners. Machine-learning
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for projections. This project aims to explore the coupling of the ocean and ice-sheet model components via a machine learning emulator of ice-shelf cavity circulation. While the ultimate goal of the project is to