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biological data is an advantage, but is not required if you bring strong machine-learning expertise and are motivated to learn the biology. Our offer a central role in developing ARCA, the core AI technology
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microbiome and genome data to learn contextual representations of microbes and communities and translate them into predictive models for successful crop microbiome engineering. Your job Plant-associated
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: Within this international project, TU Delft will develop a machine learning-based forward operator to enable the assimilation of SAR imagery into the crop growth model. You will: Process SAR imagery over
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Physics Informed Machine Learning method which exploits the advantages of physics-based and data-driven models, while mitigating the disadvantages. This research will contain experimental and modelling
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for support in technology development. You will work closely with a PhD researcher at the German partner who focuses on the underlying machine learning models, and you will help coordinate the joint work across
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across a range of application areas, including education and healthcare. As these systems are increasingly deployed in high-stakes environments, there is a growing need for machine learning models
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economy. As a postdoctoral researcher, you will unravel how silicon suppresses liquid copper infiltration at the atomic scale, using density functional theory-accurate machine-learned potentials and
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processing and statistical data analysis. Familiarity with flow modelling techniques (CFD) or machine learning for fluid flows. Aptitude for team work and excellent communication skills in spoken and written
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21 Jul 2026 Job Information Organisation/Company University of Twente (UT) Research Field Engineering » Biomedical engineering Engineering » Computer engineering Researcher Profile Recognised
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Vacancies PostDoc Research Position on Developing Technology for Subjective Sporting Experiences Key takeaways Can technology learn to listen to how athletes feel, and not just to what the sensors