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(Department of Physics) and the Bio-Imaging Lab (Department of Biomedical Sciences) are looking for a full-time doctoral scholarship holder in the field of super-resolution diffusion MRI for predicting
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PhD Scholarship in Design optimizer tool and predictive control system of a Megawatt Charging System
. • You specialize in the following topic: Development of a design optimizer tool and predictive control system of a Megawatt Charging System and local grid; • You work on projects independently
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stages is especially challenging due to limited data availability, making it difficult to predict environmental impacts accurately. Additionally, complex synthesis routes require integrating green
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the impact of molecular rearrengements on the performance, shelf lifetime and stability of electronics. Our goal is to define a physical framework capable to predict relevant timescales.The successful
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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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related to staff position within a Research Infrastructure? No Offer Description PhD position available: Development of data-driven methodologies for the prediction of part performance. Details: https
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to biological data sets such as omics data, protein structure prediction, or biomedical imaging. Technical experience in programming (Python preferred), and/or machine learning is a plus—not a requirement. We
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to biological data sets such as omics data, protein structure prediction, or biomedical imaging. Technical experience in programming (Python preferred), and/or machine learning is a plus—not a requirement. We
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on the development of new methods to accurately predict cavitation in heavy liquid metals through a combination of computational and experimental approaches. The computational aspect involves developing advanced CFD
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the preparation and defence of a PhD thesis in the field of Medical Sciences. More specific, you will work on the validation of predictive peptide biomarkers to classify non-small cell lung cancer (NSCLC) patients