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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 2 months ago
Europe Research and Innovation Programme under the Grant Agreement No.101178775 Workplan: Design, develop and implement deep learning methodologies for generation of subsurface earth models. Duration
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artificial intelligence (i.e. machine, deep and reinforcement learning…) to optimize efficiency, improve safety, reduce costs and promote sustainability. Collaborate with multidisciplinary teams to uncover a
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000 individuals in UK. As part of a wider strategy to develop a deep human experimental medicines platform, this project aims to mainly focus on using novel human in vitro platforms using IBD gut stem
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advancing social justice, equity, and civic engagement. Qualifications Basic qualifications (required at time of application) PhD (or equivalent international degree) or enrolled in a PhD (or equivalent
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of almost 11,000 individuals, including approximately 7,700 academic staff members, who passionately pursue answers to the profound questions that shape our future. Fueled by curiosity and a deep sense
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PhD project, the successful candidate will develop an open-source workflow using deep learning and hierarchical statistical models to streamline the data flow from acoustic recorders to ecological
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: 10-May-2026, 11:59:00 PM Disclosure and Barring Service Requirement: Please note that the successful applicant will require DBS disclosure. Further info: https://bit.ly/3DP5K8y This post is for
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. ESSENTIAL REQUIREMENTS A PhD inMachine Learning, Computer Vision, Computer Science, Physics, Engineering, Mathematics or related areas. Documented expertise in: Machine/Deep Learning, and possibly Computer
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record in NLP. The ideal candidate should be able to demonstrate broad knowledge of deep neural architectures as applied to core NLP tasks. The position presupposes a PhD degree or equivalent in Natural
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at the intersection of mathematics and AI safety, with a focus on developing rigorous mathematical foundations for AI interpretability. Research directions include mean field theories of deep learning, data attribution