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combination with machine learning and/or data mining techniques • Explainable AI/ML using visualization • AI/ML-empowered visual analytics of multivariate networks (network embeddings, …) • Large Language Model
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data reflect real‑world disease phenotypes. Advanced analytics: apply AI and machine‑learning techniques (e.g., graph neural networks, multimodal transformers) to uncover novel biomarkers and generate
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focus is the interplay of these factors with mitochondrial translation systems and respiratory chain complex assembly. We use the yeast Saccharomyces cerevisiae as our primary research model. In
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through the application of both well-established statistical modelling and newer machine learning methods. The research specialist will be integrated in the computational team led by John Wallert
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conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in large language models (LLMs
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datasets for analysis. Implementing and improving deep learning models for detecting and mapping forest disturbances. Validating model performance using reference datasets and ground truth information from
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northern Europe. Our research covers a broad spectrum of fields, from core to applied computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in
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collected can be trusted for training machine learning (ML) models and run-time interference. Both the use of AI in products, as well as the collection of data, assume fast iterations that allow for rapid and
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of surface sites makes theoretical understanding difficult. This project will develop and benchmark machine learning models to predict local electronic density of states (DOS) at alloy catalytic sites
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combination with machine learning and/or data mining techniques • Explainable AI/ML using visualization • AI/ML-empowered visual analytics of multivariate networks (network embeddings, …) • Large Language Model