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Field
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We are looking for a highly motivated PhD candidate interested in AI-based methods, including machine learning and language technologies, for the integration and analysis of clinical, advanced data
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, and registry-linked outcome data. In this project, you will develop and apply AI-based methods (e.g. machine learning methods and many other methods) to harmonize historical and current pathogen
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of ion conductivity in complex battery materials on a large scale. Model‑generated data will be used to identify key relationships between material structure and ionic conductivity through advanced data
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their PhD. Project description The aim of this project is to deepen the fundamental understanding of machine learning through the lens of optimal transport theory, systems theory, and statistical physics
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 9 days ago
and fine-grained semantic information within the prompts, and assess geometric accuracy of corresponding models' answers. If necessary, we will then propose dedicated learning strategies for inducing
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, machine learning, deep learning, high powered computing (requiring Python etc) or a combination of data science and qualitative methods (e.g., interviews and focus groups). Project themes include (but
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The Department of Electronic Systems at The Technical Faculty of IT and Design invites applications for PhD stipends or integrated stipends in the field of Machine Learning for Intelligent Hearing
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About the Project As federated learning systems become increasingly embedded in high‑stakes domains where data cannot be directly shared, such as healthcare and finance, the ability to selectively
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(MPC) and Reinforcement Learning (RL) have proven effective in isolated studies, their widespread deployment is hindered by the lack of interoperability, the high cost of model creation, and data
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group in Utrecht University, which focuses on multimodal computer analysis of human behaviour. As a PhD candidate, you will be part of a vibrant inter- and transdisciplinary research community