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excursions. The PhD student will work on analysing the reliability of paleomagnetic records, deriving uncertainty estimates, and building time-dependent models of the paleomagnetic field evolution using a
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: Approximately 2,000 EUR/month for three years Website: IMPRS-ESM Application Contact: office.imprs at mpimet.mpg.de The International Max Planck Research School on Earth System Modelling (IMPRS-ESM) invites
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phenomena such as the spread of misinformation or the formation of filter bubbles. For this, we rely on rigorous probabilistic methods to model and analyse the intrinsic complexities of these systems
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results from numerical modelling of surface mass balance, firn compaction and ice flow dynamics identifying and quantifying processes of ice sheet change and ice mass balances developing stochastic
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integration of energy systems data and models and apply data science methods to make them usable for transparent energy systems analyses. The collected data will be processed and semantically enriched using
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scientists on, e.g.: Developing self-supervised learning frameworks to extract features from unlabeled high-resolution microscopy data Training and evaluating segmentation models for detecting and
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master’s degree or diploma in physics, applied mathematics, or a relevant engineering discipline Good programming skills and experience with numerical modeling Interest in performing experiments Excellent
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Engineering, Operations Research, Civil Engineering, Computer Science, Data Science or a related field, from a university/department with a strong international research reputation Strong mathematical and
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) Research area: Large Language Models (LLMs), knowledge graphs (KGs), commonsense knowledge Tasks: foundational or applied research in at least one of the following areas: LLMs, KGs, knowledge extraction
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to model and analyse the intrinsic complexities of these systems. This research direction requires advancements in modern probabilistic tools, including spatial random graphs, random walks, and Markov chains