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their Viruses to Assess Peatland Health and Predict Restoration Success - CArBiVoRe”. Specifically, as part of your PhD research, you will explore microbial responses to the restoration of agricultural
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sustainable materials, (d) Artificial Intelligence (AI) models to predict and control the manufacturing process and (e) a Digital Twin (DT) incl. Building Information Modeling (BIM) information backbone
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production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures. Your research will focus on addressing current bottlenecks in data and
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platforms can unify production environments, enabling predictive maintenance and data-driven optimization through centralized data platform architectures. Your research will focus on addressing current
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restraint conditions. A key goal is to develop both a sensor system and a prediction model for the short- and long-term deformation behaviour of concrete. These tools will be applied to full-scale structural
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process. An integral part of the project will be the development of enhanced data-driven physics methods to achieve reliable prediction of material removal rate and material removal distribution
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the project will be the development of physics enhanced data driven methods to achieve reliable prediction of residual usable life of milling tools. The approach will be validated by application to industrial
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trustworthy but also adaptive in the face of unpredictable operational conditions. The successful candidate will have a solid foundation in AI methods for predictive and/or proscriptive problems and will
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involving protein structure prediction by AlphaFold 3 combined with crosslinking/mass-spectrometry and single particle cryogenic electron microscopy on native or recombinant TZ complexes, you are expected