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Field
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: Advancing machine unlearning, privacy-preserving techniques, and robust data curation. AI Safety: Ensuring robust alignment and safety in multi-agent LLM systems Efficiency: Streamlining large-scale model
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renovation. Impact: Enable robust decisions for sustainable buildings. Job description Delft University of Technology is hiring a PhD researcher on “Uncertainty-based Life-Cycle Modelling for Building
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for probabilistic unsupervised learning for structured biological data. The successful candidate will: Develop probabilistic factor models and scalable inference algorithms for structured biological (multi-view) high
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for geophysical flow modelling, uncertainty quantification, and high-performance computing. Its work relies on both deterministic and statistical approaches. The objective of this PhD project is to develop a new
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models that bridge the gap between laboratory settings and natural environments. It fits with Sustainable Transitions axe of the TIRIS program (https://anr.fr/ProjetIA-22-EXES-0015 ), addressing challenges
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methodological comparison between mechanistic vector control models and macroscopic SIR-type models calibrated on serological survey data. Currently, risk indicators (e.g., R₀ estimation) derived from mosquito
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to the deep ocean. These canyons are the conduits for transport of land-derived materials to the ocean floor in avalanche-like events called turbidity currents. Turbidity currents transport enormous amounts
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Funding for: Competitive Worldwide Funding Funding amount 4 year University Scholarship starting 27/28 academic year. Minimum tax-free stipend at the current UKRI rate (for 2025/26 standard stipend
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demonstration of a DNA-based stack data structure and recent advances in optimisation for DNA nanotechnology, the project will design, model, implement, and experimentally validate novel molecular data structures
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funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description In this project, we develop a new mathematical framework to efficiently model