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developed by the project partners will be based on two key technologies: machine learning algorithms that generate artificial yet realistic data points (synthetic health data) and secure multi-party
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innovative methods for processing and analyzing 7Tesla MRI images of different modalities and formats (NIFTI, DICOM, etc.) using machine learning and artificial intelligence techniques. These methods will be
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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supervisors, from different universities: Dr. Georgios Tsaousoglou at DTU, and Dr. Maryam Kamgarpour at EPFL, Lausanne, Switzerland, with the opportunity to undertake an extended research stay at EPFL. Project
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on the use of Machine Learning algorithms for rapid damage assessment. Research topics could focus on: the definition and use of novel damage sensitive features, physics informed machine learning, transfer
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and Saeys teams. In this research project you will develop and apply algorithms to link clinical phenotypes of metastasis to molecular phenotypes in mouse models. It is known that metastases exhibit
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, transcriptomics (bulk, scRNAseq) epigenetic profiling (methylome, Atac-Seq, Chip-seq) and proteomics, using computational methods. Algorithm Development: Designing and implementing algorithms to process, analyse
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to the development of advanced control and energy management strategies for different energy systems. The SEA-POWER project, conducted in collaboration with VEBRAT E-Mobility Solutions, aims to investigate and enhance
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on using unstructured and overset meshes with high-fidelity algorithms to obtain scale-resolved data. Candidate will also post-process data using data-driven and physics-driven methods to extract fundamental
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. This is achieved by working closely together with the different wind farm operators within the Belgian offshore zone, e.g., Parkwind, Norther, Otary. The main focus of the department is on performance