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to announce a joint open PhD position in Improving Machine Learning Methods for Contingent Claim Pricing and Hedging. The project will focus on, but will not necessarily be limited to, a promising relatively
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The project you will work on lies on the boundary between AI and theoretical physics. Physics has been a source of inspiration for innovation since the early days of machine learning. In particular
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Are you interested to work, in an interdisciplinary research setting, on topics at the intersection of logic, machine learning and automated reasoning? The Institute for Logic, Language and
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Are you eager to apply cutting-edge machine learning techniques, develop innovative algorithms, and tackle real-life challenges associated with diagnosing of Alzheimer’s disease? The Business
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Do you enjoy solving challenging machine learning problems on complex real-world data, and are you interested in the fintech (financial technology) domain? The AMLab group at the university
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Do you enjoy using computational approaches to study biological problems? Are you interested in using a combination of machine learning and mechanistic modelling? We invite enthusiastic and
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. What do you have to offer? You have strong analytical and critical thinking abilities, you are enthusiastic and self-motivated when it comes to learning new skills or exploring new topics, and you have
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students. Tasks and responsibilities: Conducting independent research in physics and machine learning, resulting in academic publications in peer-reviewed international journals; Help to guide Master
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electronic experiments at cryogenic temperatures; Develop theory and computer simulations to support the experiments; · present your results in international workshops and conferences; Collaborate with other
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PhD Cognitive Neuroscience: "The Influence of Predictions on the Mechanisms of Conscious Perception"
distinguishing between effects on decision-related vs. truly perceptual processes. EEG and fMRI will be used, in combination with machine learning and other AI tools, to measure the effects of predictions