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Field
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statistical model calibration, machine learning and data analysis. The research environment is international and interdisciplinary, with close links between fundamental method development and technically
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experience, deemed equivalent by the GRC (or delegate). The ideal PhD candidate will have: A strong background in machine learning, deep learning, and signal processing Proficiency in Python and machine
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when using closed, proprietary models, where model weights, training data, and internal representations are inaccessible. The PhD project will therefore investigate how trustworthy agentic AI systems can
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flow theory or machine learning frameworks (e.g., PyTorch, TensorFlow). Strong written and oral communication skills in English. Personal characteristics To complete a doctoral degree (PhD), it is
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machine learning and AI, probabilistic risk modelling, hydrology and actuarial science. We realize that candidates will usually have expertise in one of these fields and ask for a genuine interest in the
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. Experience of working with large multimodal datasets. Interest in human-computer interaction and human-centred system design. Strong communication and organisational skills. While it is not necessary to have
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systems, or continuous-time and discrete-time LTI systems theory is a plus. Experience with mathematical modeling, optimization, numerical computation, algorithm development, or machine learning. Prior
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partners in the Netherlands and abroad. You will work with large, multi-center clinical datasets and contribute to translating advanced computational methods into clinically relevant applications. This PhD
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Knowledge of machine learning, Large Language Models (LLMs), Vision Language Models (VLMs), or generative AI Experience with Retrieval-Augmented Generation (RAG), AI agents, model-driven engineering, DevOps
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be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably