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of Barcelona; Particle Physics Phenomenology group. Main responsibilities / tasks: 1. Develop anomaly detection methods using Machine Learning and Simulation-Based Inference for high-dimensional parameter spaces
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-off companies. CONTEXT AND MISSION We are seeking a postdoc to join the Quantum Machine Learning team (QML-CVC) in beautiful Barcelona. The QML-CVC team (https://qml.cvc.uab.es /) is part of
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and modelling of omics, clinical and imaging data, development of reproducible pipelines, application of machine learning techniques, integration of multi-modal data, scientific publication and
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include an FC150 bump-bonding machine, a Pac Tech SB2 solder deposition machine, an F&K Delvotec automatic wire bonding machine, and a dedicated lead-shielded room for sensor characterization with radiation
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based on neutral atom platforms, exploring both theoretical and experimental domains. Research will span quantum control, quantum-enhanced machine learning, and hybrid quantum-classical computation
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. Recognised Researcher position has been opened. The ideal candidate holds a master's-level background in robotics, AI or related fields, with strong Python/C++ skills and experience in machine learning
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software. (0-35) Experience in the application of advanced machine learning techniques (e.g., graph neural networks, reinforcement learning, probabilistic models, or latent representations) to biomedical
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the neurovascular space. Knowledge of neurovascular anatomy, acute stroke, endovascular treatments, neuroendovascular devices for the treatment of stroke. Ability to generate machine learning analysis of medical
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AI4Science project, specifically focusing on the intersection of advanced machine learning and sustainable catalysis discovery. The primary incentive of this Postdoctoral Fellowship is the chance to contribute
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expertise in machine learning or computational modelling who are eager to advance conceptual innovation toward practical industrial deployment. Qualifications PhD in Computer Science, Machine Learning