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Field
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models that merge machine learning techniques with mechanistic frameworks (like physics-informed neural networks and grey-box modeling) to enable predictive simulations of chemical and biochemical
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Oxford Nanopore Technologies (ONT). Your role will be central in creating and applying bioinformatics and machine learning tools to analyze long-read data and decipher cap-specific signals from raw
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for individuals preparing for academic careers in statistics, biostatistics, machine learning, or related fields. The postdoctoral researcher will be supervised by Dr. Razieh Nabi. For inquiries, please contact
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, and other methods. Collects data using cameras, sensors, and data acquisition systems. Prototypes, troubleshoots, and fine-tunes machine learning models for outcome predictions relevant to surgery
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the LAMP group at the Computer Vision Center (CVC), in Barcelona, Spain. The position is for 2-3 years and linked to the project “Foundations for Adaptive and Generalizable Deep Learning” (EXPLORA
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: Develop and implement machine learning algorithms for SOC and SOH estimation. Analyze large datasets from battery systems to improve model accuracy and performance. Conduct research on predictive analytics
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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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well as companies and governmental organisations . They will contribute to the activities of the wider machine learning and data science research group and write up the results of their work, with co-authors
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possible for up to 1 day/week. You will join an interdisciplinary team of researchers spanning imaging science, machine learning, genetics, and population health, working closely with collaborators
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Requirements Research FieldComputer science » Computer systemsEducation LevelPhD or equivalent Skills/Qualifications Knowledge • Solid understanding of machine learning, deep learning, and modern AI techniques