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modelling and numerical relativity. The group comprises PI Alicia Sintes, a further ten faculty members and a large number of post-doctoral researchers and PhD students. The group is a member of the IAC3
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We are seeking a postdoctoral researcher with a curiosity-driven record who works at the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy
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establish a research profile. Develop and execute innovative research projects. Develop, train, and evaluate modern machine-learning models on GPU/HPC infrastructure. Integrate AI methods with scientific
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on health data as well as training AI models on health data across national borders. Key responsibilities Design, implement and benchmark machine learning models for large-scale health datasets consisting
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of Research Experience1 - 4 Additional Information Eligibility criteria We are looking for a doctor in particle physics with less than two years of experience after the PhD. Experience in machine learning and
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Institut d’Investigació Biomèdica de Girona Dr. Josep Trueta (IDIBGI-CERCA) | Spain | about 2 months ago
will begin from June 2026. Education and Background PhD degree in a relevant quantitative discipline such as Bioinformatics, Statistics, Mathematics, Data Engineering, or Biomedical Engineering
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development and evaluation. This opportunity will prepare candidates for a range of competitive positions in academia or industry that involve machine-learning for biological or chemical data, computational
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scale. Increasingly, our work integrates AI and machine learning approaches to process and interpret large and complex Remote Sensing datasets. When joining our group, you will also join the wider and
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Researcher in The School of Chemistry. The InTeleCat project involves the use of machine learning and AI as applied to organic synthesis. It is a large collaborative project involving researchers in the US
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, Security & Privacy, USENIX Security, CCS, etc. More generally, the project is part of a large initiative at Serval and SnT, which aims to support the reliable deployment of machine learning systems by