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application! We are seeking a highly motivated PhD student to join a research project at the forefront of battery diagnostics and modelling, that will help shape the future of battery technology by developing
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. You will develop dynamic models and apply them, for example, to analyze sociotechnological networks and to model interactions between humans and AI agents (such as LLM-based chatbots and autonomous
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methods that can accurately model such processes remains an open and active research frontier. This PhD project is fundamentally about advancing that frontier, contributing new methods for generative
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-language-action models (VLA), specifically the handling of uncertainty in VLAs. VLAs have the potential to simplify system design in robotics and autonomous driving, both through verbal user interfaces, and
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will build an experimental and computational platform based on 3D-printed, brain-mimetic tissue models with tunable transport properties, where interface transport can be measured and predicted
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equations. Your main research assignments will be to develop new models and methods for generative sampling and Bayesian inference. You will be jointly supervised by Assistant Prof. Zheng Zhao (https
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emissions and biodiversity impacts related to bank lending, particularly in agriculture. Therefore, banks therefore often must rely on coarse standard values or low‑precision models, making it difficult
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distributed wireless systems" which is conducted in collaboration between Linköping University (LiU) and Lund University (LU). Read more here: https://elliit.se/project/machine-learning-for-sensing-in
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application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through scaling model sizes, training budgets
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biology of infection. For more information, please see https://www.scilifelab.se/data-driven/ddls-research-school/ The future of life science is data-driven. Will you be part of that change? Then join us in