217 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" positions in Sweden
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Optimal Transport for Optimization and Machine Learning Appl Deadline: 2026/02/04 11:59PM (posted 2025/12/19, listed until 2026/02/04) Position Description: Apply Position Description Doctoral student in
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machine learning models in simple, standalone devices that are capable of advanced processing. Building on our work on solution-based neuromorphic classifiers (https://doi.org/10.1002/advs.202207023
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. Your profile You have documented expertise in marine ecology and computer vision and machine learning methods for video-based fish monitoring. You have excellent IT skills and experience in handling
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-disciplinary research at the intersection of artificial intelligence, robotics, machine learning, and human-robot interaction. Subject area The subject area for this position is Computer Science. Background
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software related to the medical field Experience of specific software and programming languages, specifically ones suitable for machine learning, e.g. PyTorch or TensorFlow. Strong ability in spoken and
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. Teaching may also be included, but up to no more than 20% of working hours. The position includes the opportunity for three weeks of training in higher education teaching and learning. The purpose
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and expertise in brain imaging (MRI), image processing and machine learning. Coordinating projects within the research group, supervising students and writing applications are also included in the role
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emulators for accelerated forward modeling Advanced data-intensive machine learning and AI techniques for survey analysis Applications to major international surveys, including LSST (Rubin Observatory
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electronic systems. You will also develop and teach courses in electronics design at the bachelor and master levels, and take part in the further evolution of the excellent and multi-faceted research and
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access to preventive care and neighborhood characteristics influence long-term health trajectories. The project applies both econometric and machine learning approaches to identify high-risk groups and to