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and experiments with modelling of these experiments. As part of this, we have developed new algorithms and a completely new web-based platform – EasyNMR - for performing such modelling/simulations. With
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research results (algorithmic developments, data mining techniques, etc.) to feasible deployment • Integrate the developed techniques with modern ICT/IoT and other digitalization systems • Cooperation
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analytical skills. Knowledge about statistical machine learning, robotic perception, multimodal AI algorithms. Proven experience with reinforcement learning algorithms and implementations. Experience in
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experimental validation of control strategies for the developed systems. This includes the development of advanced control algorithms capable of handling varying operating conditions, ensuring system stability
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in e.g. programming, algorithms and data structures, software systems architecture, use of AI, data acquisition and fullstack software-development. Following the Problem-Based Learning (PBL) model, you
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establish new strategies to improve the accuracy of radiotherapy dose calculations including algorithms for virtual iodine contrast removal. Overall, the project will aim to establish clinical workflows
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of two years. About the Project The successful candidate will advance the algorithmic and theoretical foundations of reinforcement learning applied to complex, high-dimensional dynamical systems
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: Designing lightweight TTA algorithms that can recalibrate models at the edge under strict latency and computational constraints. Efficiency and Reliability: Balancing the trade-offs between adaptation
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to make infrastructures and production more effective and energy-efficient, with algorithms that autonomously learn the behaviour of technical and socio-technical systems and make optimal decisions without
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, these advanced compression techniques allow for on-the-fly data compression, support analytics and machine learning (ML) without decompression, significantly reduce algorithm complexity and memory use