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Field
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doctoral programme coordinated by the UAB, designed to recruit and train 25 doctoral candidates. The programme focuses on advanced materials, innovative methodologies, and transformative solutions
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Multi-omics data integration and workflow improvement Development and application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial
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to train tomorrow’s leaders in earth and environmental science. For further details about the programme please see http://nercgw4plus.ac.uk/ For eligible successful applicants, the studentships comprises
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or incomplete. Information Your tasks will include: Developing and benchmarking ML/AI algorithms tailored to low-data regimes — e.g. few-shot learning, transfer learning or data-efficient representation learning
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reconstruction, and the need to evaluate generated and transmitted data in terms of their relevance and utility for achieving specific objectives. To address these challenges, the project will develop theoretical
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representation. Key aims include improving the generalizability, interpretability, reasoning and causal grounding of these models, developing new optimisation algorithms with biologically meaningful regularisation
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and reliability. You will integrate, adapt and develop methods (using packages such as BioSPPy) for the processing and analysis of physiological signals measured for determining deception. The project
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. Exposure to neural-symbolic algorithms for transforming intent into conformant security or safety policy and/or enforcing security controls is optional but beneficial. Research will also give the opportunity
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implementing AI algorithms to deliver safer and more efficient care. The student will have access to a unique training programme in AI in healthcare and health data science as well as a wide range opportunities
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | about 1 month ago
to be carried out focus on the daily operational activities of home hospitalization units, with the goal of developing optimization models and algorithms to support the planning of routes, schedules, and