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and application of fast solvers for Maxwell’s equations and nonlinear inversion algorithms that we have already developed in a previous PhD project. In addition to electromagnetic geophysics
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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 in a previous
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the closing date for applications. The applicant must have good programming skills, excellent knowledge of algorithms, numerical methods, and signal processing Mandatory experience and formal training: signal
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the PhD has been awarded at the latest within 5 months after the closing date for applications. The applicant must have good programming skills, excellent knowledge of algorithms, numerical methods, and
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recognition neural networks (e.g., Tsetlin Machines, Graph Neural Networks, RNNs for time-series causal discovery, XGBoost). AI-driven heuristic search (e.g., genetic algorithms, genetic programming, simulated
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intermittent. The PhD will work will be twofold. The first part will be to improve and develop datasets and estimation algorithms for renewable energy that will enhance the simulation capabilities of the open
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balance simulations that support estimates of snow distribution for biodiversity and ecosystem assessments, as well as hydrological modelling and management plans for ski resorts and hydropower companies
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complexity, the impact of blowing snow on local and regional scales. The PhD candidate will produce mass balance simulations that support estimates of snow distribution for biodiversity and ecosystem
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, will be distributed over the full employment period and thus corresponding to 25 per cent of the time each year. The employment period may be reduced if you have previously been employed in a qualifying
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About the PhD Research Fellow The fellowship will be for 3 years, with the possibility of a fourth year consisting of 25% compulsory work (e.g., teaching responsibilities at the department) distributed