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strong interest in machine learning and deep learning algorithms · Research and publication experience in health outcomes research Application Materials: · CV · Cover letter · Contact information for three
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pathway prediction. Apply deep learning techniques to predict reaction outcomes, optimize reaction conditions, and identify novel synthetic routes. Curate and manage reaction datasets from literature
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research approaches. Working closely with nursing partners and clinical collaborators, our exciting work combines non-invasive imaging technologies, deep learning, computer vision, and clinical workflow
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CBS is a globally recognised business school with deep roots in the Nordic socio-economic model. Our faculty has a broad focus on societal challenges, and we have earned a reputation for high-quality
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an employer based outside Poland; Additional requirements: experience in conducting research using machine learning techniques, supported by a body of research evidence through publications in international
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exceptional postdoctoral research fellows interested in developing deep learning and computational methods for pathology image analysis, multimodal data integration, and other medical modalities (e.g
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SD-25157 RESEARCHER IN ATMOSPHERIC PLASMA TREATMENT OF METALLIC SURFACES FOR INDUSTRIAL APPLICATIONS
of metallic surfaces. The researcher will work closely with the industrial partner to build up a deep understanding on the mechanisms driving the surface modification and develop strategies to implement
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the possibility of yearly renewal subject to funding availability. Key Responsibilities • Conduct and lead research in 3D computer vision, deep learning, and AI for digital twin generation, publish findings in top
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therapeutic targets. These efforts will generate large-scale, rich perturbation datasets, requiring the development of sophisticated approaches and methods incorporating facets such as machine learning and
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related discipline. The candidates will have expertise in computational imaging, with: (i) an algorithmic focus, with particular interest in methods at the interface of deep learning and optimisation