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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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an international cohort, receives supervision across institutions and takes part in three residential schools, methods workshops, monthly research seminars and joint work with organisations beyond academia. Every
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institutions and takes part in three residential schools, methods workshops, monthly research seminars and joint work with organisations beyond academia. Every project includes a planned six-month intersectoral
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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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supervision across institutions and takes part in three residential schools, methods workshops, monthly research seminars and joint work with organisations beyond academia. Every project includes a planned six
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they describe the forest in detail. Traditional inference methods run on finalized, clean data: but a robot needs an answer based only on partial observations. The PhD aims to address this gap, taking machine
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education, research, or knowledge-intensive industry. The position reports to the Unit Leader of Colorlab. About the project The research will address the growing need for reliable methods that can assess the
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develop quantitative methods to estimate effects on infrastructure degradation, maintenance needs, operational risk, punctuality and costs. The aim is to develop and validate a practical, transparent
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other components. The PhD candidate will link TrainGate detections and early warnings with maintenance, incident, operational and cost data and develop quantitative methods to estimate effects
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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