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the vicinity of perennial snow fields during the growing season. The PhD student will also be responsible for analysis of data from eddy covariance sensors, weather stations, and hydrological installations, as
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. The following achievements, skills and/or knowledge are considered particularly relevant: Experience in standard bioinformatic analysis related to genomic and genetics, e.g. experience with any
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the flexibility of neural methods. If successful, the work has the potential to advance applications such as automated theorem proving, knowledge-graph inference, and causal analysis. The Department of Computing
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algorithms for Bayesian machine learning with applications in e.g., medical image analysis. The doctoral student position is offered within the machine learning project “The Challenges for Machine Learning in
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of Mathematics and Mathematical Statistics, alongside Computational Mathematics, Mathematical Statistics, and Analysis & Modelling. The Discrete Mathematics group consists of 10 faculty members whose research