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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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robots, autonomous vehicles, and drones, develop rich frameworks to interact with the world in real time? This is the main underlying theme to be explored within this postdoctoral position. The appointed
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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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developing new visualisation strategies to aid delineation, as well as developing deep learning methods to enhance photon-counting CT images and better visualise tissue boundaries. The project will also
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algorithms. Implementation Expertise: Outstanding scientific programming skills (Python, PyTorch/JAX) with a proven track record of developing, debugging, and scaling complex RL pipelines or custom simulation
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of cloud-based recalibration, edge AI systems must become "self-aware" and capable of autonomous evolution. Core Research Objectives. The primary objective of this PhD is to develop a high-performance, low
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grow in complexity and scale, traditional monitoring approaches are insufficient to ensure efficient, reliable, and fault-free operation. This project will develop novel AI methods that bridge first
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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