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the development of stochastic models for decentralized energy markets, decentralized and learning-enhanced market clearing algorithms, and fair-by-design pricing strategies. The research will address one or more of
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for machine learning, with research topics ranging from decentralized and federated optimization, adaptive stochastic algorithms, and generalization in deep learning, to robustness, privacy, and security
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for the development, implementation, and theoretical study of innovative statistical models (mainly stochastic block models and latent space models) for the analysis of complex criminal networks (multilayer, multiplex
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models. Experience and knowledge of stochastic differential equations. Experience of using MHD output as input for GCR transport models Theoretical knowledge of particle transport in plasmas Documented
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understanding of the phenomenon and will include stochastic algorithms such as Markov Chain Monte Carlo enabling inclusion of uncertainty analysis and recovery of the AM onset and its rating [1] as probability
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understanding. You will be supervised by Svenja Fischer, Ryan Teuling and Claudia Brauer. Where to apply Website https://www.academictransfer.com/en/jobs/357376/phd-candidate-ml-based-predicti… Requirements
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will have an MSc or equivalent qualification in mathematics, machine learning, LLMs, and biomedical image analysis or computational psychology. You will have proven experience in stochastic analysis and
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biomedical image analysis or computational psychology. You will have proven experience in stochastic analysis and neural differential equations and with the development of stable diffusion models. Post
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chains, random graphs and trees, random matrix theory, stochastic and Lévy processes in infinite-dimensional spaces, free probability, random sphere packings in high dimensions. About the role You will
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— an interdisciplinary field spanning information theory, stochastic thermodynamics, and quantum physics — and will explore several research directions: Thermodynamic computing: Developing physics-inspired models