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into the setting of metabolic flux inference and, with inspiration from existing algorithms, develop tailored MCMC algorithms. You will implement the ensuing algorithms in an existing C++ framework, validate and
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, graph neural networks, physics-informed ML) to approximate PF results Train models using simulation results generated from conventional power flow solvers Evaluate AI-based approximators in terms
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Your Job: Join our team as a dedicated scientist and contribute to our exciting research projects. Our work focuses on models and algorithms for supervised and unsupervised learning. We devise deep
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support and training in classical and quantum modeling, quantum algorithms and computation, and battery materials and technology Further development of your personal and interdisciplinary strengths through
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and/or statistical algorithms to classify building and land-use types relevant to electrical consumption Label and prepare training data for AI models; develop automated pipelines for classification