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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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to develop 2D and 3D NMJ models for DM1, consisting of different combinations of patient-induced pluripotent stem cell (hiPSC)-derived neuronal and muscle cells, allowing the dissection of pathological
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(MSCA-PF) aims to support the career development and training of excellent researchers through international mobility. Researchers with a strong scientific background and a promising research project are
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your area of responsibility, and encouraging mobility when it serves the organisation’s interests; Encouraging the integration of newcomers and contributing to their initial development plans, in
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the role and function of active mobility (walking, cycling) and micro-mobility options (e.g., moped) to improve health and enhance sustainable mobility options. The collaboration with different European
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approaches combining control, learning, and uncertainty quantification. This project develops a data-driven control framework grounded in first-principles models with emphasis on: Data-driven practical
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uptake of GNM into APCs; evaluation of the efficacy of GNM in terms of antigen presentation and APC/T-cell crosstalk. Training in cell culture and immune activation, flow cytometry, antigen presentation in
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Human Stem Cell-based Neural Models is available at the Language & Genetics Department of the Max Planck Institute, Nijmegen, the Netherlands. The position is in the research group of Simon Fisher, co
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Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description Are you curious about complex systems and learning
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the future of quiet, sustainable flight! Background New aerospace concepts like Urban Air Mobility (UAM) introduce air taxis and delivery drones in cities that are densely populated. The human impact of