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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap
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is a viable option. The candidate will be responsible for 1) analysing long-term monitoring data from the International tundra experiment (ITEX) in Endalen after 20 years of artificial warming, 2
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measurement and stopping rules controlling type-I error with finite-sample guarantees. Quantum statistical modeling: use of quantum relative entropy, Fisher information, and variational quantum circuits
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of these materials. Implementation of artificial intelligence (AI) and machine learning (ML) to establish the connection between the existing models and material data (both literature and the baseline established in
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how large language models can generate goal-oriented robotic programs from natural language task descriptions and contextual information. Developing agentic AI architectures that allow robots to plan
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research program that aims to understand the genetic principles of complex trait plasticity and develop climate-resilient plants using innovative techniques such as omics data interpretation, gene editing
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geophysical and planetary models. IBM faces several such challenges in practice, many of which are currently approached using Foundation-Model (FM) surrogates. However, certain inverse problems arise in data
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addition, the candidate should ideally possess the following skillset: A strong security background (in cryptography, mathematics, information security, or a related field) Previous industrial experience is
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: preparing and conducting PhD data collection, teaching, attending conference(s), coursework Semester three: analysing PhD data (possibly publishing), teaching, coursework Semester four: publishing PhD results
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via our online system . Further information We offer up to 16 fully financed PhD positions. Excellent scientific and transferable skills training, competitive work contracts or tax-free fellowships, as