321 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "Ulster University" research jobs in Sweden
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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-Geometric Foundations of Deep Learning or Computer Vision KTH Royal Institute of Technology, School of Engineering Sciences Job description The Department of Mathematics at KTH welcomes applications for a
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assays, complemented by mass-spectrometry-driven chemical profiling and machine-learning-supported multivariate analysis. Where relevant, CRISPR-Cas-based genetic perturbations in mammalian cell models
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for a postdoc to join our team at the Division of Systems and Control, Department of Electrical Engineering. Become part of our innovative group and contribute to exciting research in learning-based
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The applicant must: hold a PhD in a relevant field (e.g. computer science, artificial intelligence, machine learning, computer vision, animal science, biology, veterinary medicine, or a related discipline) have
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, including time series analysis and statistics (e.g. mixed effects modelling) Capacity to develop computer code and experience with programming languages (Matlab, Python, R) and geospatial tools (e.g. ArcGIS
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by The Kempe Foundations. Project description Machine learning and artificial intelligence have had a major impact on medical image analysis in recent years. While CT and MRI provide highly
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: Applied mathematics; Machine Learning; Mathematical Modelling Appl Deadline: 2026/03/24 10:59 PM UnitedKingdomTime (posted 2026/03/18 04:00 AM UnitedKingdomTime, listed until 2026/04/01 04:59 AM
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electrocatalytic reactions, with a primary focus on the conversion of biomass-derived feedstocks into value-added chemicals. For further details of our approach look at: KTH | Paula Sebastián Pascua l and https
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measurement technique development, atmospheric modelling, and advanced methods for integrating observational and model data through data assimilation and machine learning. About the research project The overall