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, or engineering. Our research integrates mathematical modeling, machine learning, and quantitative experiments to understand and control the dynamics of microbial communities in time and space. Ongoing projects
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concept to curved, high-order discretisation, while preserving accuracy, mesh validity, and robustness. This will involve advances in CAD–CAE interfacing, curved element optimisation, multiscale geometric
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service systems working at scale in a given community in order to meet families where they already are. The TMW Center has a robust research and development strategy that includes further development and
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of the hallucination detection system under diverse visual and textual conditions Primary experiments will be conducted in CARLA (https://carla.org), enabling controlled and repeatable evaluation of hallucinations under
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Localization and Mapping) algorithms, integrating data from sources like GPS, IMU, and LiDAR to achieve robust vehicle localization. Implement, test, and tune navigation controllers (primarily the visual
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executing strong policies and protocols, implementing and monitoring evidence-based clinical practices, and mitigating risks by overseeing a robust program of risk management and quality improvement
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undergraduate and advanced levels, mainly in programmes leading to degrees in agronomy and biology. Read more about our benefits and what it is like to work at SLU at https://www.slu.se/en/about-slu/work-at-slu
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. The purpose of the framework is to apply contemporary, evidence-based strategies to reduce the prevalence of gender-based violence with three streams: Primary prevention: developing and maintaining a robust
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The School of Mechanical & Aerospace Engineering (MAE) is a robust, dynamic and multi-disciplinary international research community comprising of world-class scientists and bright students. MAE
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Computational Imaging Research Lab (CIR), Department of Biomedical Imaging and Image-guided Therapy | Austria | about 2 months ago
, startups and patient partners. Your Mission as PhD is to develop novel machine-learning methods to (1) detect small, clinically meaningful changes in joints, (2) enable robust longitudinal monitoring and