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to teach robots to understand forest well enough to navigate and move through them in real time, using machine learning on LiDAR point clouds and camera imagery for real-time understanding of the forest
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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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November 2026 at 23:59 CET Expected start: 1 January 2027, or upon agreement About REGULAIRE The scholarship is part of REGULAIRE (Regulatory Learning for the Governance of Transformative Technologies), a
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Business School Application deadline: 15 November 2026 at 23:59 CET Expected start: 1 January 2027, or upon agreement About REGULAIRE The scholarship is part of REGULAIRE (Regulatory Learning
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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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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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machine learning and advanced analytical approaches Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Show curiosity and a strong motivation for the subject
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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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data-driven learning and which should remain within structured optimization. In line with AID’s research areas, the project will emphasize knowledge embedding, uncertainty representation, risk-aware
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support. The PhD candidate will develop and validate a hybrid methodology that combines established stochastic optimization with AI-based learning. The aim is not only to develop new algorithms, but also to