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of Chemistry (Inorganic Chemistry). Our group investigates fundamental questions in bioinorganic chemistry using advanced spectroscopic techniques to understand the structure and reactivity of complex metal
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substrate, enabling the layer-by-layer construction of complex 3D objects. The temperature field created by the interaction between the electric arc and the material is a critical factor influencing the
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electrophysiology to translational models, including animal studies and analyses of human tissue samples. This full-stack methodology enables us to directly link molecular channel function with disease phenotypes
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is part of the newly funded Research Council of Finland project Redefining Inner Boundaries in Solar Wind Models (RIB-Wind), led by Dr. Stephan G. Heinemann. The preferred start date is October 2025
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the analysis of the complex data and cellular models (Big Data and Kavli Institutes). The DPhil will provide the student with multidisciplinary skills including specialized training in bioinformatics, genetic
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critical roles of ion channels—particularly the TRP superfamily—in physiological and pathological processes. Our interdisciplinary approach spans from foundational electrophysiology to translational models
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innovative pedagogies such as Challenge-Based Learning and complex learning environments by fostering higher-order thinking, transdisciplinary collaboration, and active student engagement. The research takes a
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) developing and validating preprocessing pipelines; (3) architecting and comparing spectral-only and multimodal (HSI + NIR + Raman + RGB) deep-learning models; (4) implementing robust sensor-fusion strategies
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environment Geotechnics research at the Department of Architecture and Civil Engineering focuses on the characterisation and modelling of complex geomaterials, particularly natural clays. Our work bridges
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
intelligent reasoning and feedback mechanisms into digital twin environments, enabling them to interpret complex maintenance data more effectively. Using AI techniques, such as large language models, knowledge