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research areas especially along two themes: “Platform security and application”s and “Machine learning and security/privacy ”. Our goal is to provide the environment for the successful candidate to mature
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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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measurements from real projects, statistically analyse them, and conduct experiments with modern machine learning techniques and generative AI. A strong background in software engineering as well as some
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, parental leave, appointments of trust in trade union organizations, military service, or similar circumstances, as well as clinical practice or other forms of appointment / assignment relevant
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structure and carry out work. Preferred qualifications Prior experiences of teaching and/or supervision of students' project work, knowledge in machine learning and deep learning, language technology
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that include machine learning components, and on cooperation with industrial partners and with the TECoSA competence center at KTH. The Division of Network and Systems Engineering conducts fundamental research
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statistics, unsupervised machine learning, optimisation, model predictive control. Experience in financial mathematics. Having high integrity, be process-oriented and able to work independently. Being able
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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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collaboration with Lund University. The candidate is expected to have a strong mathematical background particularly in stochastic modeling, optimization, and reinforcement learning. As a PhD student, you devote
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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