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models ignore this exposome. In BEE, we will build explainable, physics-guided, GeoAI-driven models that: Predict acute and chronic NCD risks at the population scale Identify vulnerable neighbourhoods and
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prediction models, and visualizing immense volumes of various types of data, generated by agri-robots and IoT devices. The most popular classes of autonomous agricultural devices include: weeding robots
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research in data-driven nutrition, health, and food science. With large-scale diet and health data, omics data, biomarkers, digital food and health services, we establish predictive models for evaluation
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breeding programs and to support the reduction of methane emissions; a strong interest in statistical models, genomic prediction, and quantitative genetics, preferably with experience with one of more
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for control” funded by the EU Partnership on Animal Health and Welfare (EUPAHW, https://www.eupahw.eu/ ). Supervisors Dr Timothée Vergne is an Associate Professor of Veterinary Public Health at the National
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28 Feb 2026 Job Information Organisation/Company KU LEUVEN Department ORSTAT/FEB Research Field Computer science » Database management Computer science » Modelling tools Economics » Business
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Optimization (AI/ML) Developing AI/ML models to predict drillability issues based on mechanical rock properties Real-time parameter optimization (WOB, RPM, flow rate, etc.) using machine learning techniques
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energy system models based on the institute`s own open-source FINE framework https://github.com/FZJ-IEK3-VSA/FINE. Your tasks in detail: Implementing geothermal plants with material co-production in
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reports to develop computational models that predict identification reliability. They will learn to design interpretable, legally robust AI systems, including attention-based deep learning models and
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already been awarded a PhD degree. Selection process You should submit your CV through a dedicated site: https://cv.newton-6g.eu/ Additional comments Position: Data-driven models for CF networks