72 machine-learning "https:" "https:" "https:" "https:" "https:" research jobs in Sweden
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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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Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work may include clinical and biomedical projects. It may also include technique development
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Machines, (IMOL), Poland, and the Leicester Institute of Structural and Chemical Biology, United Kingdom. Your work may include clinical and biomedical projects. It may also include technique development
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manufacturing. It is meritorious to have previous experience in data analysis and processing with Python (or similar), preferably including documented experience with machine learning tools. It is meritorious
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located at SciLifeLab in Stockholm. Our research is focused on cell biology, spatial proteiomics and machine learning for bioimage analysis. The aim is to understand how human proteins are distributed in
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that support the unit for area protection and marine spatial planning, as well as operations at SLU Aqua. Your profile You have documented expertise in marine ecology and computer vision and machine learning
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scientific curiosity Mastery of data visualization and scientific communication Extensive knowledge of relevant machine learning and AI techniques Self-motivated individual with ability to work independently
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description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources
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will (micro-)benchmark Java-based applications using JMH. You will collect performance measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning
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into mechanosensory corpuscles (Nikolaev et al., Sci Adv 2020; Nikolaev & Ziolkowski et al., Sci Adv 2023; Ziolkowski & Nikolaev et al., Sci Adv 2025). This is just the beginning and there is still much to be learned