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hiring a doctoral candidate on the subject "Foundation AI models for distribution systems decision-making ". Foundation models have recently emerged as a new learning paradigm in AI. These models learn
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focused on the development of next-generation compact hyperspectral spectropolarimetric cameras. Hyperspectral imaging enables simultaneous acquisition of spatial and spectral information, supporting
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have enabled rapid progress in many fields of engineering and science. In many modern applications, particularly in advanced 3D imaging assisted characterization, sensing, simulation, vast amounts
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prediction of DNA replication defects and mitotic errors in human IVF embryos from live cell imaging data. In addition, you will contribute to teaching activities (maximum 0.1 FTE) and supervise Bachelor and
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enabled pathway for high value secondary metabolites. By generating and integrating multi-omics, imaging and process datasets, the project will enable AI to (i) accelerate callus line development through
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an experimental aerodynamics team has concentrated on the development of advanced non-intrusive measurement techniques such as Particle Image Velocimetry, InfraRed Thermography and Background Oriented Schlieren
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team investigating densely-laden pipe flows. In the project, you will use Magnetic Resonance Imaging and Velocimetry to provide insight into the velocity and concentration profiles. This technique
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machine learning. You will develop and evaluate AI-driven visual speech recognition models and contribute to their integration into a smart-glasses prototype. The system aims to convert non-vocalized lip
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households or companies. But energy data is not like images or text: it consists of time series living on a physical network, governed by power-flow equations. Off-the-shelf generative models produce data
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imaging. The department provides access to modern experimental facilities, including multiple eye-tracking and combined EEG and eye-tracking laboratories. The project also benefits from access