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international experience during their PhD. We offer a PhD student position to explore and develop Machine Learning models evolving over space and time and to make use of these models to understand
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these models in real tissue context, revealing how spatially organized cell communities rewire under genetic perturbation. Key challenges Use and improve single-cell foundation models to predict SNP-driven
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Software Development: Building the Next-Generation Trust Maturity Model Integrating DevOps practices into ML-driven systems: A Framework and Maturity Model for Continuous Machine Learning Development
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funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description This PhD project will explore a
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interdisciplinary PhD projects in the Simulation and Data Lab Digital Bioeconomy. Each project combines natural sciences with computational and data-driven approaches, focusing on topics such as plant carbon
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models such as Random Forest and Neural Networks to help understand and predict pairwise interactions between pollinators and plant species. - Software Engineering: integrate models into a standalone
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these models in real tissue context, revealing how spatially organized cell communities rewire under genetic perturbation. Key challenges Use and improve single-cell foundation models to predict SNP-driven
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computational and data-driven approaches, focusing on topics such as plant carbon transport, microbial systems, or circular bioprocesses. You will contribute to developing and applying novel modeling strategies
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attracting highly qualified talent. We look for researchers from diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security
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topics: Software engineering practices for machine learning Tabular machine learning Large language models on structured data PhD Student Role: Under the direction of their supervisor, the candidate will