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Field
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participate in a project which investigates and develops novel immunotherapies (cell therapies) to cancer, utilizing both mouse and human systems. Applicants should possess a PhD degree or be close to
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highly stimulating environment that engages the best and brightest faculty and students to conduct deep and impactful research. Our faculty's research expertise and strengths cover several key
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qualities You hold either PhD in deep learning techniques and an interest in climate science, or a PhD in Meteorology or Climate Science having clear experience with deep learning techniques. You possess
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, concentration and functional inequalities • Mathematical aspects of machine learning and deep neural networks • Free Probability aspects of Quantum Information Theory. While excellent candidates with other
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and future climate scenarios—including counterfactuals—and integrating them with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty. The candidate will
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chatbots and other virtual assistants that can help students learn more effectively or prepare for exams, or support teachers in repetitive tasks. We are looking for a highly motivated and experienced
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A PhD in immunology or related field with a deep understanding of mucosal immunology is required. Expertise working with primary human cells, intestinal tissues/organoids, murine models of intestinal
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datasets Proficiency in Python for data science and machine learning Possess sufficient breadth or depth of specialist knowledge with deep learning architectures including generative models, particularly
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with health surveillance and related data to estimate climate-attributable risk under Deep Uncertainty. The candidate will also contribute to the development of interactive tools and training materials
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data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand