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learning to automatically build models from experimental data while directly embedding physical and mathematical knowledge within the learning architecture. This hybrid approach produces more reliable models
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algebra, algebraic geometry or model theory. The position will be funded by Jan Stovicek's project Homological and model-theoretic methods in algebra and geometry awarded by the Czech Science Foundation
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reactor modeling and accelerate the scale-up of emerging net-zero technologies? Join us at the Department of Chemistry and Chemical Engineering ! About us Our research group at the division of Chemistry and
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starts. Preferably, you will also have: Interest in global water issues and earth system modelling; Strong quantitative methodological skills, for instance knowledge of (spatial) data analysis
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, certification, and/or registration. Specific knowledge, skills and abilities required to perform the job satisfactorily include: Demonstrated experience developing and/or implementing machine learning models in
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Infrastructure? No Offer Description Work group: IBG-4 - Bioinformatik Area of research: PHD Thesis Job description: Your Job: Chromatography modeling, while crucial for modern bipporcess development, still
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
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expertise in numerical modelling. The post is available from 1st March 2026 for 12 months in the first instance and could be extended further. See http://rccs.hw.ac.uk/ for more details. Key duties and
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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model's development. The position is based at LOCEAN on the Pierre and Marie Curie campus of Sorbonne University. The LOCEAN laboratory (https://locean-ipsl.upmc.fr ) is one of nine laboratories in