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Life and Health Sciences Research Institute (ICVS), from the School of Medicine (EM) of the University of Minho | Portugal | 3 months ago
of the University of Minho. Fellowship duration: The fellowships will have an estimated duration of 6 months, starting predictably in September 2026. In accordance with the FCT Research Fellowships Regulation
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well as resource limitations. The core research objective of this PhD is to design and evaluate “latency hiding” methods for immersive networked interactions. This involves (i) developing predictive machine learning
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ecosystems where interconnected multi-agents interact strategically in dynamic and uncertain environments. While Artificial Intelligence (AI) optimizes predictions or policies, energy systems are inherently
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clinical data for prediction and biological discovery Prospective clinical sequencing to guide the care of cancer patients Studies of coding, non-coding, RNA, and spatial-based drivers of cancer development
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and limitations arising from the use of AI-based methods in predictive feedback. The successful candidate will: explore how a combination of multimodal observation (audio, video, LIDAR, thermal vision
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questions. Implements and adapts machine learning and AI-based approaches for high-dimensional epidemiological data, including variable selection, prediction modelling, and data integration. Retrieves
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simulations of compact binaries (including, for example, binary black holes, binary neutron stars, and black hole–neutron star binaries). The broader goals are to generate accurate predictions for gravitational
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, e.g. HPLC, ICP-MS. Experience with using nuclear-reaction codes which are commonly used to predict the reaction cross sections for medical isotope production, e.g. TALYS, TENDL, CoH, ALICE and EMPIRE
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dynamic and uncertain environments. While Artificial Intelligence (AI) optimizes predictions or policies, energy systems are inherently multi-agent, strategic, and resource-constrained. Each agent has its
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of limited temporal and spatial accuracy of such remote interactions. We pay particular attention to the exploration of potential and limitations arising from the use of AI-based methods in predictive feedback