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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 8 days ago
from the combined risks of supply disruption and the importance of these materials for economic and technological development. The general objective of this project is to advance knowledge
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come from one of the following countries: https://www.ugent.be/nl/onderzoek/financiering/bof/dos/countrylist.pdf More information about the scholarship: https://www.ugent.be/en/research/funding/bof/dos
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ecosystems. The project combines targeted experiments on ionizing radiation, metals, and drought with quantitative modelling, validated on a NORM/legacy case study. The outcome will be an assessment-ready
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psychology, and nuclear research to develop and test innovative approaches for countering false information. Using a mixed-methods, multi-level design, it will combine digital analytics, experiments, surveys
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–microbe interactions, combined with selected circular soil amendments, specifically phosphogypsum residues and biochar- can affect the leachability, retention, and bioavailability of radionuclides in
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equivalent alternate combination of relevant knowledge, training and/or experience; At least two years of professional experience working in a client- or customer-facing role in a management consulting firm or
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boundaries of precision medicine by developing a clinical microscale dosimetry framework for PSMA-targeted RPT in prostate cancer. The innovative core of the project lies in combining high-resolution
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safer energy future, this PhD offers a unique opportunity to combine theoretical rigor with practical impact - while gaining expertise in reactor physics, advanced numerical methods, and high-performance
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to the future of safer, adaptive cancer treatment. The project combines cutting-edge science with a clear clinical mission: to make radiotherapy systems that can think, predict, and protect — in real time. Where
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 4 days ago
resolution mammography images. The model should be trained using a combination of weakly supervised learning, for images without local annotations, and fully supervised learning, for images with corresponding