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processes Implement models and perform numerical simulations Analyze and interpret simulation and experimental data Integrate AI tools and workflows to enhance modeling and accelerate learning Collaborate
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bachelor’s, master’s and PhD degree programs within animal science and veterinary medicine. We offer a lively, engaged and innovative learning and study environment, which is closely integrated in the research
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, pharmacology, genomics and multi-omics, as well as growing methods in advanced analytics of health data e.g. machine learning to improve human health with a focus on therapeutics. These posts will work alongside
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. The simulations will be part of a collaboration within the EU project SHiFT in relation to the Iceland Deep Drilling Project. Your responsibilities: Conduct original research on high-enthalpy geothermal systems
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complex mixtures. Day-to-day responsibilities will include designing and fabricating electrochemical reactor prototypes, conducting electrodeposition experiments in deep eutectic solvent (DES) media
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communications, orthogonal time frequency space modulation, radar/sonar signal processing, and machine and deep learning. WORK PERFORMING: Individual will perform research generally in the area of statistical
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training of the assigned faculty supervisor. This role requires a solid foundation of discipline‑specific knowledge in plant physiology, CT imaging, and deep learning/AI, and the ability to generate
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at least one of the following: a PhD degree related to circular design innovation in the circular economy, preferably in the area of apparel/textiles; or equivalent experience in a different sector; or a
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, biomedical engineering, medical imaging, or related field Experience in deep learning with practical implementation Strong Python skills and relevant frameworks Experience with large clinical imaging datasets
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Research Center for Molecular Medicine (CeMM), ÖAW | Vienna, Virginia | United States | 2 months ago
on LazySlide ( et al Nature Methods ), our scalable software foundation, and our deep learning framework for age prediction (Abila et al., Nature Medicine, in press) to engineer a body-scale machine learning