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Professor research work will include the following topics and tasks: Develop algorithms and theory for inversion of data collected by RIMFAX and other CENSSS instruments. Contribute to modelling, inversion
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the methodologies for preventing unintended, harmful behaviors in open-source AI models. Your work will focus on the foundational challenges of safety, from mitigating algorithmic bias to ensuring systems remain
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borehole electromagnetic data during drilling. This includes the further development and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed
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democracy design experiment: Making the UK exam algorithm controversy game. Design Studies, 91, 101245. Hicks, B., Kitto, K., Payne, L., & Buckingham Shum, S. (2022). Thinking with causal models: A visual
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develop new deep learning algorithms for spatio-temporal medical image analysis with particular focus on learning from limited labelled data. General information about the position. The position is a fixed
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algorithms. Analyzing results and contributing to scientific publications in peer-reviewed journals (expected 3 papers), presenting at meetings and international conferences. Taking part in the teaching as
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. The goal is to contribute broadly to research on applications of AI in medicine, and in particular to the development and validation of novel computational language models, algorithms, and tools
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algorithms to tackle complex dynamical systems. This PhD offers the opportunity to work in a vibrant research environment at the Department of Engineering Cybernetics and NORCE, alongside experts in control
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algorithms. Analyzing results and contributing to scientific publications in peer-reviewed journals (expected 3 papers), presenting at meetings and international conferences. Taking part in the teaching as
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algorithm controversy game. Design Studies, 91, 101245. Hicks, B., Kitto, K., Payne, L., & Buckingham Shum, S. (2022). Thinking with causal models: A visual formalism for collaboratively crafting assumptions