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Are you interested in understanding and modeling human capabilities to shape the future of autonomous systems? We are looking for a motivated PhD student to join an exciting research project focused
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Ice and mixed-phase clouds remain a significant challenge for both observational methods and atmospheric modeling. However, recent advances in both areas offer promising opportunities for progress
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Research Infrastructure? No Offer Description Are you interested in understanding and modeling human capabilities to shape the future of autonomous systems? We are looking for a motivated PhD student to join
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chemical engineering processing, material recycling, nuclear chemistry, theory and modelling. We have expertise in Chemical Engineering, combining experimental research with modelling techniques. Our
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learning workflows, and developing complete models. Example applications include drug design, cryo-electron microscopy, structural prediction and dynamic simulation of biological macromolecules, genomics
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focus on technology foresight developing data-driven approaches for probabilistic modelling of new technologies; a second will focus on policy analysis leveraging machine-learning approaches
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to the development and use of new in vitro model systems for neurodegenerative diseases and brain cancer. The group is led by Henrik Ahlenius and includes 3 postdocs, an associate researcher, and a number of master’s
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analytical and mathematical skills, including proficiency in quantitative modeling, data analysis, and scientific computing (e.g., R, Python). Strong written and verbal communication skills in English. *for
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, optimizing, and deploying AI models on HPC and GPU-based systems. Provide guidance on performance optimization, scaling, and efficient resource utilization. Contribute to architectural and design decisions in
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principled new models and methods, for modern machine learning problems. Machine learning recently has been largely advanced by differential equation-based frameworks, such as generative diffusion models