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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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reinforcement learning. The project will investigate a simulation platform that reproduces the structure of real prospection activities by integrating multiple, heterogeneous geospatial and archaeological data
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offers the opportunity to turn advanced photoemission into a routine, high-impact capability for probing the chemistry and electronic structure that govern tomorrow’s semiconductor technologies. Today
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systems have identifiable material, structural, or spatial implications. The aim of your work is to contribute to the project by helping to establish a framework for understanding the relationships between
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controls, by contributing to the construction and commissioning of new compact seismic isolation systems for Advanced Virgo+ Phase 2. A major upgrade planned for the O5 observing run is the installation
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that leverage state-of-the-art AI methods (deep learning, generative AI, Bayesian modelling, active learning, etc.) to combine cellular imaging data, chemical compound structure, viral genomes and other omics
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structures) and the incidental learning of new patterns. To this end, we employ a range of methods, with a focus on behavioral experiments and electroencephalography (EEG). What are your tasks? At least 70% of
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integrity and scientific rigour. Fluency in written and spoken English. In vitro skills Hands-on experience with iPSC derivation, QC, maintenance and differentiation. Experience with 3D and co
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integrity and scientific rigour. Fluency in written and spoken English. In vitro skills Hands-on experience with iPSC derivation, QC, maintenance and differentiation. Experience with 3D and co
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, microbiological, physicochemical and structural aspects, research into nutrition and health is an important and complementary pillar within the department. To strengthen research and teaching in the field