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for downstream tasks. In this project, you will develop novel unsupervised machine learning methods to analyse cardiovascular images, primarily focusing on MRI. In your research you will train models to learn a
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candidate with a strong background in computational fluid dynamics (CFD) and specialized expertise in hemodynamics associated with coronary artery disease (CAD). The ideal candidate will hold a PhD in
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in the SPG. We will make use of models of different complexity up to complex Earth System models, and modelling efforts for different past periods. A personalised training programme will be set up
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cybersecurity allowing thus to validate and receive feedback from on-the-field cybersecurity practitioners. As generative AI (GenAI) platforms and large language models (LLMs) are increasingly integrated
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– from the modeling of material behavior to the development of the material to the finished component. PhD Position in Machine Learning and Computer Simulation Reference code: 50145735_2 – 2025/WD 1
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specimens. Key Duties & Responsibilities Generation of Experimental Research Data using Mouse Model and Human Samples. Analysis of High-Dimensional Computational Data. Manuscript and Grant Writing
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to remote sensing and simu-lation modeling. A particular focus of our work is on mountain forest ecosystems. A quantitative understanding of ecosystem dynamics provides the foundation for the development
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focus on solid dosage forms and 3D printed drug products. The research will integrate advanced imaging, computational modelling, and pharmaceutical sciences to improve the resolution, reproducibility, and
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models on edge devices Reporting findings and methods in conference and journal papers Your profile Masters, Diploma or equivalent degree in IT/computer science/statistics/applied mathematics/data science
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Senior Researcher in Synthetic Biology and Metabolic Engineering of power-to-X utilizing Microorg...
. Responsibilities and qualifications Your overall focus will be to establish and lead a strong research program in the field of synthetic biology and metabolic engineering of non-model microorganisms, with the aim