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projects and newly-proposed projects, development of new statistical methods that address specific questions arising in cancer research, and eventually, scientific leadership of a biostatistics shared
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to climate neutrality of the power, industry and building sector. This involves empirical, theoretical and numerical methods. For the position, experience with questions relating to electricity market design
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with a considerate link to applications in the engineering or natural sciences. Possible but not exclusive areas are applications in the engineering and natural sciences modern methods of numerical fluid
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attention is paid to the following Knowledge and skills in numerical analysis, specifically numerical methods for differential equations and numerical linear algebra are required. Knowledge in applied
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are essential. You should have expertise in at least one of the following areas: theoretical modelling, machine learning, artificial intelligence, or numerical methods for climate systems, and be confident in
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, engineering, materials science, maths, or computer science), or equivalent experience Experience with uncertainty quantification or error analysis Familiarity with numerical methods (e.g., Monte Carlo, Finite
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statistical methods. We are committed to supporting and developing our team members, through attendance at conferences and access to training in research methods, and will support you to further develop your
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or a numerate discipline OR equivalent experience. Broad knowledge of probabilistic models, Bayesian inference and machine learning methods. Good knowledge of R, Python or both (links to project source
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the theory of quantum graph states. Additional expertise in computational methods would be useful but is not necessary. The Postdoctoral and Senior Research Associate positions will also involve
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of new EEG and MEG neuroimaging and mc-tCS simulation approaches based on realistic head volume conductor models using modern finite element methods as well as sensitivity analysis. The new methods will be