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) validation and extension of models for the prediction of enzyme-substrate interactions; (iv) integration of the previous models into deep learning pipelines for retrobiosynthesis. Applicable legislation and
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measurement science research in robotics, advanced autonomy, and artificial intelligence systems. Utilizing deep learning, large language models (LLMs), reinforcement learning, and unsupervised machine learning
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excellence across CEPS. You will contribute to CEPS’ visibility and influence through public speaking, media engagement and thought leadership activities. Where to apply Website https://cdn.ceps.eu/2026/07
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leader • Excellent written and oral communication skills Preferred Qualifications • Background in antisemitism studies • Experience with R, NLP and deep learning libraries • High performance computing
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required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential. Additional Qualifications
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sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve
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related to creating or testing deep learning models for genomics, exploring new techniques related to spatial simulations, or other topics discussed with the PI. Basic Qualifications Core job duties include
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project “Actively learning experimental de-signs in terrestrial climate science (ACTIVATE)”: https://www.mn.uio.no/geo/english/research/projects/activate/index.html The PhD fellow will be part of a growing
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Institute for Research in Mathematical Sciences and the Principles of Intelligence (PrincInt: https://princint.ai/ ), housed within the Fields Institute's Centre for Mathematical AI. The fellowships support
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, scikit-learn, PyTorch, TensorFlow); additional experience with R, MATLAB, or Julia is an advantage. Machine Learning Expertise: Familiarity with causal machine learning, ensemble methods, and deep learning