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Field
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automatic detection, segmentation and classification of sub-canopy archaeological features such as earthworks, burial mounds, walls, etc. – Investigation of interpretable/explainable AI methods, supporting
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-center clinical datasets and contribute to translating advanced computational methods into clinically relevant applications. This PhD position offers a unique opportunity to work at the intersection
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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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the following conditions: 1. Scientific Area: Engineering 2. Admission Requirements: i) Student enrolled in a Doctoral Program in Electrical and Computer Engineering or related fields; ii) Suitability
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. For this, we apply a combination of methods from both Physics and Mathematics, complemented by concepts from Theoretical Computer Science. Our research group is located jointly at the Faculty of Physics – as
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estimated from observed data. The overall aim of the project is to develop statistical theory, methodology, and computational methods for such complex data problems, with a particular focus on models
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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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interest are not directly observable. These may include, for example, ability, attitudes, well-being, or different dimensions of poverty, which instead must be estimated from observed data. The overall aim
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scanning and measurement, garment fit evaluation, cloth simulation, digital twins, wearable sensing, smart textiles, and AI methods for clothing and body data analysis, to name a few. Applicants from