16 learning-"https:"-"https:"-"https:"-"https:"-"https:"-"https:"-"UCL;" Postdoctoral positions in Switzerland
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than 18,500 people, including over 14,000 students and 4,000 researchers from more than 120 different countries. Postdoctoral Position in Computational Genomics, Machine Learning & Single-Cell Biology Mission
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. Olga Fink ). IMOS develops advanced machine-learning and AI methods for complex engineering and industrial systems, with a particular focus on improving their reliability, availability, and operational
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at EPFL is seeking for talented and ambitious postdoctoral scientists with strong fabrication skills (or a big interest to acquire such skills) to work in the young and dynamic field of Integrated lithium
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on developing new generative modeling approaches, scalable training algorithms, and foundation model technologies. The role is suited for candidates with a strong machine learning background who are excited
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PhD in one of these areas. Candidates should have an exceptional academic record, a strong background in machine learning, and a robust mathematical foundation. They are also expected to have strong
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, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out
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improve high-throughput experimental workflows including closed-loop thin-film optimization Apply AI and Machine Learning for data analysis and modelling Develop, improve and implement HW/SW concepts and
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and industrial project partners. You will be responsible for: Developing and adapting machine-learning approaches for structure-based and generative molecular design. Integrating physicochemical
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and associated environmental impacts. Contribute to short-term (2026 to 2030) and long-term (2030 to 2050) verticalisation forecasting models based on machine learning, and to their validation against
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(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor