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theory, modeling, and AI-assisted optimization activities within the consortium. Reporting and dissemination of the results. Share this opening! Use the following URL: https://jobs.icfo.eu/?detail=1074
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. Key Responsibilities: Develop and implement perception and control algorithms for robotic arms and embodied AI systems. Assist in integrating multimodal AI models (vision, language, force sensors) with
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adaptability of robotic systems. In particular, the research will investigate distributed and/or game-theoretic control frameworks for multi-robot systems, integrating optimization-based planning with learning
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NEST: https://nest-simulator.readthedocs.io Your tasks in detail: Work with the NEST main code base and experimental branches Dissect the spiking network simulation cycle into phases and capture the flow
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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be negotiated based on the selected candidate's availability. Group or Departmental Website: https://we3lab.stanford.edu/ (link is external) How to Submit Application Materials: Please apply via
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quantum chemistry (DFT) and control engineering. PhD position in chemical reaction engineering (Kinetic modeling & thermal runaway) Supervisors: Sébastien Leveneur (sebastien.leveneur@ircelyon.univ-lyon1.fr
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offer a deeper analysis of this class of stochastic processes concerning their stochastic and statistical analysis and to propose some non-Gaussian stochastic models, based on (generalized) Hermite
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to offer a deeper analysis of this class of stochastic processes concerning their stochastic and statistical analysis and to propose some non-Gaussian stochastic models, based on (generalized) Hermite
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subsystems into high-power high-voltage converter systems. Prepare comprehensive technical documentation, including design/test reports, control models, test results, and source code. Work closely with cross