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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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micro/nanoCT, confocal microscopy and SEM Characterize fibrous scaffold architecture, porosity and structural stability Develop computer vision and machine learning methods for multimodal bioimaging data
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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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doctoral degree Collect, structure and assess relevant sensor, operational, maintenance, incident and cost data Develop and validate statistical, causal and/or machine-learning methods and turn the results
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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
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, operational, maintenance, incident and cost data Develop and validate statistical, causal and/or machine-learning methods and turn the results into useful decision support Publish and communicate results and
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prepared for changes to your work duties after employment. Required selection criteria You must meet the requirements for admission to the Doctoral Programme in Computer Scienc e, see Section 6-1 of the PhD