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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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and cancer immunotherapy. Main responsibilities: Conduct studies on the mechanisms of immunogenicity against CAR-T cells in patients treated with CAR-T therapy. Perform studies on immune reconstitution
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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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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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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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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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, etc.) o Energetic frustration or protein energy landscape analysis o Machine learning in protein science o +2 years of experience after PhD Knowledge of evolutionary biology concepts (phylogenetics
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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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performance with classical methods. Qualifications PhD in Physics, Computer Science, Applied Mathematics, or related fields. Strong background in at least one of the following: machine learning, quantum
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platforms. Experience in development of digital twins or physics-informed machine learning models. Experience in programming (e.g., Python or equivalent) and development of control or data acquisition