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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 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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fellows will receive joint mentorship from leading experts in metabolic biology, AI and machine learning, drug delivery, and translational medicine, while maintaining full academic independence in research
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Learning and parameterised quantum circuits. Analysis of multi-omic data for the identification of therapeutic targets. Migration of algorithms from classical simulators to IBM quantum hardware
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communication skills; ability to work effectively in an international environment. • Demonstrated ability and motivation to learn new techniques and procedures. • Experience with control and data acquisition
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-hand learning experience. Personal growth, innovation and learning every day. ● The selected candidate is expected to join the IFAE as soon as possible. Application and Selection process: Applications
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projects are continuously developed. Continuous learning and career development pathways. Flexible working hours. 23 days of annual leave plus 9 personal days. Flexible remuneration programme, including meal
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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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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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learning and the use of robust statistics. This work is naturally extended to studying physics prospects for the next generation of detectors. IFAE is supported by its own PIC computing center, a Tier1 LHC