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Field
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close collaboration among colleagues within and across disciplinary boundaries, efficient decision-making processes and a vibrant research environment involving senior researchers, doctoral students and
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life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular processes to human health
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communication systems for 5G and 6G, channel modelling, channel characterization, radio-based positioning and sensing, and associated signal processing algorithms. Work duties Employment as an assistant professor
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retrainable pipelines, model-wrapping services, LLM prompting logic, data pre/post-processing components, and explainability hooks. The research will examine how design patterns, principles, and quality
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experiences large disturbances. A particular focus will be on characterising large-signal dynamics and analysing the transient stability of wind farms, taking into account the physical limitations of both wind
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(CFD) of new solutions for capturing biogenic CO2 from point sources. The work will consist in creating simulations tools based on CFD to simulate details on CO2 capture by solvents in complex geometries
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of studies indicate a clear connection between oral health and general health, and there is a great need to develop this research area. Despite this, knowledge and communication between odontology and medicine
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to measure these backgrounds in data. The project also aims to explore to which extent machine learning methods can help with these tasks, e.g. object reconstruction and signal vs background discrimination
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colleagues within and across disciplinary boundaries, efficient decision-making processes and a vibrant research environment involving senior researchers, doctoral students and lecturers. The department offers
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close collaboration among colleagues within and across disciplinary boundaries, efficient decision-making processes and a vibrant research environment involving senior researchers, doctoral students and