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http://www.cds.hku.hk . The Position The position is open to all research areas in Computer Science, Artificial Intelligence, Data Science or a related field, including but not limited to: AI / IoT
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. Experience in, or willingness to learn data-driven approaches, including artificial intelligence (AI) and machine learning (ML) models, to solve problems. The Successful Candidate Will A curious scholar with a
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We are seeking to appoint a Research Assistant who will work on the ELOQUENCE European project which aims to study Large Language models in Artificial Intelligence. College / Directorate College
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worthiness of autonomous vehicles cyber risk management, advanced threat intelligence secure-by-design for IoT and policy governance of cybersecurity For more details, please view https://www.ntu.edu.sg/cysren
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technologies Agentic AI frameworks, LLMs, RAG, MCP, scalable AI architectures and develop solutions at the forefront of modern artificial intelligence. You will join as the Senior AI Full-Stack Developer where
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, statistics and probabilities • data science, machine learning, artificial intelligence • optimisation • power system management, integration of renewables • energy forecasting Expected level in french : bon
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 11 hours ago
the credible use of artificial intelligence, especially generative AI and large language models (LLMs), to accelerate climate mitigation while safeguarding integrity in climate commitments. This Research
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Università degli Studi di Roma Tor Vergata - Dipartimento di Scienze Chirurgiche | Italy | 3 months ago
project aims to develop and train an artificial intelligence–based multiplex network for individualised risk stratification in prostate cancer. By integrating genomic, transcriptomic, clinical, pathological
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emphasizes statistical inference within high-dimensional testing frameworks and incorporates robust feature selection into artificial intelligence techniques. The team collaborates closely with the Stanford
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and