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conversion reactions. The second position is focused on modelling stability of electrocatalyst materials. The aim is to develop a framework to predict metastability of catalyst materials. Among the methods
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requirement. The applicant must be interested in working in an interdisciplinary research environment. The position includes close collaboration with project bioinformaticians developing genomic-based models
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Build a microservice-based digital platform enabling plug-and-play analytics apps. Create user interfaces to simplify deployment of ML models on the platform. Develop hybrid predictive maintenance models
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AI models. Identifying relevant modalities to enhance prediction performance, with a focus on multi-spectral sensors, will be a key research area. Additionally, anomaly detection for modalities other
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for optimizing metals microstructures in-situ during the AM process as well as ex-situ during post-AM treatments and enable predictions of the microstructural evolution, and thus changes in properties, while AM
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of prior data?) Additional research topics may include: Algorithmic Transparency and Fairness in Funding Decisions Comparative Analysis of Funding Models AI-Driven Predictive Analytics for Funding Success
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predictive maintenance models combining physical and ML approaches. Test, validate, and integrate developed solutions in real industrial environments. You must have a two-year master's degree (120 ECTS points
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generation by developing ML-based dual stabilization techniques. These techniques aim to predict and control the behavior of dual variables, reducing oscillations and improving the efficiency of the iterative
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measured data, apply necessary filtering and selection of data features to be stored. Couple the numerical model and the measured input data to establish a model that can predict the outcome in terms
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expression and purification of proteins, crosslinking/mass spectrometry, fluorescence microscopy, CRISPR-mediated gene editing, protein structure prediction, structural modelling, transmission electron