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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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functional managers whenever needed; Driving the development of people within your area of responsibility, facilitating the development of people beyond your area of responsibility, and encouraging mobility
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and democratic participation of citizens. You will focus on developing adaptive learning systems that enhance the transparency and contestability of AI decisions through personalized, multimodal
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participation of citizens. You will focus on developing adaptive learning systems that enhance the transparency and contestability of AI decisions through personalized, multimodal explanations. Your job AI is
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Sciences, and Health Sciences. Through our bachelor’s and master’s degrees, Professional Learning & Development programmes, and interdisciplinary research themes – including Emerging Technologies & Societal
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of the following subjects: scalable data management, systems for machine learning, distributed and parallel systems, or cloud-based systems. We are especially interested in researchers who build working systems and
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systems increasingly provide personalized recommendations in domains such as nutrition and lifestyle. However, many recommender and prediction systems rely heavily on opaque machine learning techniques
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industry. You will teach and supervise Bachelor, Master, and PhD students in Computer Science programs. In addition, you will play an active role in the cybersecurity scientific community by publishing
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Research & Development – love to be in the laboratory and aim to set up experimental facilities. You have experience and/or willing to learn hands-on cell testing via electrochemical characterisation (i-V
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Description Job description The dot on the horizon is a hybrid intelligent system in which AI tools support and learn from people interacting to make sense of something. What that something is can be anything