307 machine-learning-"https:" "https:" "https:" "https:" "https:" "University of St" "St" research jobs in Sweden
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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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: 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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learning-based control within a collaborative and dynamic environment. About us At the department of Electrical Engineering research and education are performed in the areas of Communications, Antennas and
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year. You should have knowledge and experience in bridging quantum and classical machine learning, and be fluent in English, both written and spoken. Assesment criteria Qualifications that are considered
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Join us at the forefront of life science AI. We are looking for a postdoctoral researcher to develop cutting‑edge, multimodal transformer‑based deep learning methods to extract insight from genomic
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are looking for a postdoctoral researcher with a strong background in speech synthesis and machine learning, with an interest in accessibility and human communication. Your Role You will: Plan and lead
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, incorporating their own ideas and experience in computer vision, machine learning, and related fields, to further visualization and interpretation of molecular images. Our research environment focuses
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to contribute to a positive work environment. We also value the ability to work independently in carrying out work tasks, as well as openness to learning new skills and taking on new responsibilities. We value
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than 1,800 employees and nearly 21,600 students. We are looking for a Postdoctoral Researcher in Robotics and Artificial Intelligence (RAI) (https://www.fieldrobotics.eu/ ) based at Department
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%). You will work at the intersection of numerical analysis, uncertainty quantification, and scientific machine learning. The research will primarily focus on probabilistic methods for data-driven model