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to protect AI models against data leakage during inter-departmental information sharing. With the National Police heavily relying on sensitive data exchanges, this research will develop secure machine learning
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enhance real-time decision-making in road traffic management. The project aims to bridge the gap between recent advances in AI and machine learning, in particular, multimodal and instruction-tuned
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pathology applications, including the assessment of kidney biopsies. The innovative application of machine learning in clinical settings creates a vibrant and inspiring research environment. You will be part
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in Computer Science, Artificial Intelligence, or related field. Solid programming and development skills (Python, Git, Bash). Experience with machine learning (e.g PyTorch/TensorFlow). Strong interest
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applications and machine learning. Good analytical skills and a positive attitude towards interdisciplinary work. We offer you, following the Collective Labour Agreement for Dutch Universities: A salary of
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such as malicious advertising, digital skimming and stealthy online tracking. By leveraging recent advances in machine learning and browser automation, the project aims to provide tools, techniques and
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proactively. Experience in design, prototyping, basic programming, AI and/or machine learning are a plus. International PhD candidates with scholarships below the applicable IND income standard (currently
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: MSc in materials science engineering. Backgrounds in chemistry, physics, computer science or a related area are also welcome. Good expertise or strong interest in numerical modeling, machine learning
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. The research will combine computational modeling (e.g., NLP, machine learning, deep learning) with human-centered research (e.g., user studies, experimental design, qualitative analysis). We are looking not only
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for computationally demanding applications and machine learning. - Good analytical skills and a positive attitude towards interdisciplinary work. Additional Information Benefits We offer you, following the Collective