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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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of knowledge-driven models, leveraging Bayesian statistics and causal inference for calibrated uncertainty, distribution-shift detection, and safety guarantees. You will be will working within the Center
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interpretability methods, causal inference, or multi-agent systems is advantageous but not required The successful candidate will have the unique opportunity to contribute to establishing a new research group on AI
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datasets to be used in the project's experiments. - Benchmarking of membership inference and model memorization techniques. - Development of gradient-based approximate machine unlearning approaches
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Full-time: 35 hours per week Fixed-term: 6th August 2026 for up to 5 years ECAT-i (Edinburgh Clinical Academic Track – inclusive) is the University of Edinburgh’s prestigious academic track
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background and exceptional research track records in the field of in photonic technologies, solid state physics, nanotechnology, quantum optics, topological physics, artificial intelligence or other relevant
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are open to candidates from all countries and all creative and performing arts disciplines. Applicants will have a track record of proven academic excellence and will have already made significant personal
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, fintech, smart logistics, digital policy, and inclusive growth. Publish in top-quality venues and build your track record Secure external research grants with dedicated support Engage with our thriving
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Machine Learning Seminar Group Advanced Tutorial Lecture Series on Machine Learning Non-Parametric Bayes Tutorial Course (October 9, 16 and 28, 2008) Bayesian statistics in other labs Machine Learning and
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: Preference will be given to candidates: With a track record of publications related to task planning for robotics. With training and experience in the use of deep learning and large language models