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- Institut de Físiques d'Altes Energies (IFAE)
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infrastructure (e.g. Observatorio del Roque de los Muchachos) Hands-on training in cutting-edge techniques, from detector R&D to advanced data analysis and machine learning. Attendance to international
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infrastructures. Opportunity to gain experience learning first-hand. Personal growth, innovation, and learning every day. Sending applications implies the candidate’s consent to IFAE to treat their provided
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the interplay between mutations, energetics, and evolutionary constraints, including epistatic effects. · Developing or applying machine learning approaches to predict or redesign frustration patterns in proteins
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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
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to gain experience learning first-hand. Personal growth, innovation, and learning every day. The selected candidate is expected to join the IFAE as soon as possible. IFAE is an equal opportunity employer
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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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Infrastructure? No Offer Description Experimental postdoc. Develop experimental approaches to studying learning in individual mammalian cells, following two potential paths. First, by following up the studies
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). A dynamic scientific environment of excellence, where cutting-edge biomedical projects are continuously developed. Continuous learning and career development pathways. Flexible working hours. 23 days
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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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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