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-learning–based segmentation, classification and tracking for microbes and microgels in phase-contrast and fluorescence images Optimise these models and pipelines for real-time performance and integrate them
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human cell systems, and in vivo models. We routinely work with ex vivo primary cells and patient-derived specimens and employ humanized mouse models to enable mechanistic studies and forward genetic
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frameworks. Familiarity with machine learning or AI methods applied to imaging. Research Data Management and documentation practices Statistical Analysis & Experimental Design Technical Communication (reports
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of a call for awarding a research fellowship (RF) in the scope of the research project AQUALEARN – Machine learning-based digital twins for real time anomaly detection in water supply systems. 3
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, and to use of the most recent computational advances, such as Artificial Intelligence and Machine Learning (AI/ML). The Principal Research Scientist (Managing) will provide scientific and technical
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or machine learning. Excellent programming skills in Python and deep learning frameworks A collaborative mindset and interest in socially impactful research. Experience with sign language data, multimodal
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Responsibilities will vary depending on the Fellow’s background, but may include: Developing machine learning, optimization, or simulation models to improve clinical operations and resource allocation Advancing
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areas: Developing and training robust machine learning surrogates to replace computationally expensive high-fidelity simulations, enabling exploration of vast design spaces. Formulating optimization
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the Manchester BHF CRE: Geometric Deep Learning for Complex Manifolds: Novel deep learning theories, models and architectures to simulate interactions within non-Euclidean, patient-specific cardiovascular
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. Research in the lab is highly multidisciplinary and quantitative, requiring development and use of cutting edge computational modeling and statistical analyses (including machine learning and artificial