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. This postdoctoral position at KTH focuses on developing advanced modelling frameworks and techno-economic analyses within two national research projects addressing virtual energy sharing and large-scale
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laboratory processing of root data Assist in ongoing field sampling campaigns Conduct basic data analysis Collaborate with PhD researchers from the modelling and experimental teams to link data and model
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bioinformatics for immunology research programs. You'll work at the cutting edge of AI-enhanced immunology, applying deep learning, foundation models, and advanced machine learning approaches to understand how
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engineering models for large-scale quantum computers. The aim is for this thesis to develop fundamental expertise in quantum physics and computing, and then share it with other teams in the Q-loop project, so
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30 days of vacation plus additional days off (e.g. between Christmas and New Year`s) FLEXIBILITY: Flexible working time models, including options close to full-time ( https://go.fzj.de/near-full-time
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entitled “Beyond Data-Augmentation: Advancing Bayesian Inference for Stochastic Disease Transmission Models”. The overarching aim of the project is to develop the next generation of statistical tools
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IT4Innovations National Supercomputing Center, VSB - Technical University of Ostrava | Czech | 16 days ago
was installed at IT4Innovations in 2025. For more details, see www.it4i.eu . Activity description: · modelling and optimization of electrical networks using open source tools (preferably Julia or Python
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-based techniques (e.g., deep neural networks) will be used to automatically learn the system dynamics and the modelling errors, as well as to obtain an automatic tuning of the cost parameters/constraints
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, the project will: Develop novel, modular statistical solvers to integrate domain-specific knowledge directly into latent variable models. Account for spatial structures, physical laws, high-dimensional imaging
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methods for modeling exposures, we will establish real-world patterns of hormonal contraceptive use and mental disorders, and identify potential moderating factors such as medication type, age at first use