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propagation problems, stochastic partial differential equations, geometric numerical integration, optimization, biomathematics, biostatistics, spatial modeling, Bayesian inference, high-dimensional data, large
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) workflows for learning from large-scale imaging and molecular data Develop ML models to investigate cellular responses, particularly in cancer cell lines Develop DL models for molecular design based on time
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methods when limited data is available. Large neural networks are known to be heavily inefficient in this limit, and we aim to discover better methods for this purpose. We would like to study how prior
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and calibration of reports from various sources. Collect and analyse large-scale cross-industry accident data using FRAM (Functional Resonance Analysis Method) within LLMs to identify human-, technical
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within the Data Science & AI division (DSAI). With 30+ nationalities and strong industry/academic ties, we offer a dynamic, collaborative ecosystem. The AI and Machine Learning in the Natural Sciences
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in a large project rather than on your own in a single project. The following experience will strengthen your application: Experience in energy storage, organic synthesis, or materials characterization
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track record of industrial and academic collaboration globally and has published a large number of papers on 2D materials in Nature Communications, Advanced Materials, Advanced Functional Materials
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solutions that pave the way for a sustainable planet and healthy humans. Research topics span from food chemistry and food technology to molecular and data- driven nutrition (Precision Nutrition). In Food
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construction. Information about the research environment The PhD student will join the research environment Architecture, Media, and Material Practice (AMMP) at the Department of Architecture and Civil
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The position's field of research focuses on developing and implementing safe, transparent, and explainable AI systems using multimodal deep learning and Large Language Models (LLMs) for healthcare