92 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" uni jobs in Sweden
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for modelling various cognitive processes on a neuroscientific basis, which are tested using robots. Areas of study include perception, memory, learning, cognitive development, attention, motor control and
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reconstruction. We will use physics modeling, machine learning and experiments to develop new and improved methods for using data from energy-sensitive x-ray detectors to improve the diagnostic quality of x-ray
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. The main research problems include mathematical theory, algorithms, and machine learning (deep learning) for inverse problems in artificial intelligence, as well as application to medical problems. About the
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of Decision and Control Systems is currently seeking up to two doctoral students with a strong background and interest in machine learning, mathematics, and learning for control. The successful candidates will
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-Physical Systems, you will teach at undergraduate and postgraduate level, including courses in computer architecture, embedded software, real-time systems, and AI-based perception for cyber-physical
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5 Dec 2025 Job Information Organisation/Company KTH Royal Institute of Technology Research Field Computer science » Computer architecture Computer science » Programming Computer science » Other
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We invite applications for a Doctoral student position in applied mathematics and machine learning for urban 3D reconstruction, within the Digital Twin Cities Centre (DTCC). The project aims
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are considered as other qualifications: Practical experience from statistical signal processing or machine learning. Experience with mathematical modelling and analysis of wireless communication systems. This also
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an interest in Bayesian statistics, applied probability theory, computational mathematics, machine learning, and generative AI, and offers the opportunity to contribute to a rapidly growing research field with
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experimental platform and combine it with continuum modeling of complex materials and machine-learning-based analysis methods to understand and predict biofilm structure and growth. Supervision: Shervin Bagheri