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17172 (COMPETE2030-FEDER-00864900) co-funded by the ERDF - European Regional Development Fund through Innovation and Digital Transition Program - COMPETE 2030 under the scope of Portugal 2030 and by
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barriers and enablers for cVPP adoption across diverse BC communities (including low-income, vulnerable, and Indigenous groups); (2) develop a technically feasible BC Hydro–to–cVPP coordination framework
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OF THE WORK PROGRAMME AND TRAINING: 1) Development of workflows and algorithms to complement datasets of connected data spaces, to improve analysis results (forecasting, analysis of financial tools, predictive
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, 2025), Science Immunology (2023, 2024), Molecular Cell (2025), Cancer Discovery (2019), JEM, PNAS, and Trends in Immunology. Dr. Ma’s research has been recognized with multiple national and institutional
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of Job Trainee position to pursue research as a vital component of their educational mission and professional career development. Works under minimal supervision with some latitude in the use
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or computational frameworks. Strong interest in method development, including algorithms and/or scientific software development, with a willingness to contribute to codebases or tool creation. Evidence of
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developing innovative solutions for multi-modal LLMs. Self-motivated and able to work independently, managing multiple tasks and projects in a fast-paced environment. Strong collaborative skills, with
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algorithmic foundations of quantum adversarial machine learning, an emerging field at the intersection of quantum computing and machine learning. It investigates how the unique capabilities of quantum computing
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the following tasks: Development of new large language models for process modeling Development of new decision-making algorithms Provide regular project updates to principal investigator and funding agency Report
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-leading database of MRI images of childhood tumours and have developed AI approaches to diagnose different types of tumour. To be useful for patients, this needs to be delivered in hospitals in real time