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computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties to biomaterial performance and
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microscopy and SEM, with computational image analysis, computer vision and machine learning. The aim is to develop robust and standardized methods to link structural, mechanical and biological properties
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, research institutes, industry, public agencies, and leading global institutions. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early-stage
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, control, AI, machine learning, physics, and related fields, including early-stage researchers eager to contribute to this emerging scientific frontier. About the project The role of the PhD candidate will
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Doctoral Programme, see Section 6-1 of the PhD regulations for more information. You must have a master's degree or equivalent in computer science, artificial intelligence, machine learning, computer vision
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focus on combining simulations using spatial-genetic-demographic individual based models (e.g., using the software SLiM), machine learning approaches, and genomic data to estimate larval dispersal
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CAD tools Finite-element analysis and simulation Rapid prototyping and additive manufacturing Signal processing and data analytics, control systems Machine learning or computer vision Product
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Signal processing and data analytics, control systems Machine learning or computer vision Product development in multidisciplinary engineering teams. Responsibility teams Exposure to globally leading
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mechanisms that integrate queueing theory, traffic modelling, machine learning, and network-performance prediction for improving latency, reliability and fairness to support mission‑critical services
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Machine Learning, Reinforcement Learning, AI-based time-series forecasting English language skills, both written and spoken, corresponding to the scale C1 in the Common European Framework of Reference