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data and archaeological signatures (e.g., circular mounds, linear ditches, rectangular foundations, etc.) tailored for AI applications – Feature engineering and representation learning to enhance
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. – knowledge of computer vision; knowledge of deep learning architectures; – Knowledge of C++, Python, Matlab; – Analog/digital circuits IC design capability; – Testing of electronic devices and systems; FPGA
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, Physics, Electrical Engineering, Communication Engineering, or equivalents; – Knowledge in artificial intelligence, statistical and machine learning, complex systems, agent-based modeling and simulation
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process data locally while ensuring efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices
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look for correlations between transient phenomena in the ionosphere and seismic events. The successful candidate will develop and apply state-of-the-art machine learning techniques to enhance
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assurance, data science, data-driven modelling, digital manufacturing workflows, and Digital Product Passport B.3 Hands-on experience in machine learning, ontologies and knowledge graphs, IIoT and digital
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, Python or similar) Specific Requirements Interest in optical imaging and interferometry. Prior knowledge will be a plus Interest in computational methods, image processing and machine/deep learning. Prior
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the structural and electronic properties of complex materials. Using first-principles simulations, machine learning techniques, and advanced Monte Carlo methods, the student will develop predictive
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international secondments, network-wide training events, and interdisciplinary research activities Acquire highly sought-after expertise in microfabrication, photonics, materials science, and optical sensing