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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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to learn, pro-active and responsible attitude, intellectual independence. Excellent command of English and Italian. Italian is likely to be needed as some projects will focus on Italy. Fluency in Spanish is
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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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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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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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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