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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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: 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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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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Pharmaceutical Roundtable. In this project we will employ deep learning-based protein sequence design tools to deliver biocatalysts for peptide synthesis. These designed enzymes will be further optimized using
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month 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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Department Community Environment & Policy Department Website Link https://publichealth.arizona.edu/ Location Tucson Campus Address Tucson, AZ USA Position Highlights Manage, conduct, and support firefighter
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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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them on. We welcome students, faculty and staff from every background and perspective into a community where everyone feels seen and heard. We have deep-rooted mindfulness for the natural world and all
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comparing supervised and unsupervised methods (e.g., regularized regression, tree-based models, ensemble methods, clustering, dimensionality reduction) and deep learning approaches Developing and applying
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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