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Are you interested in developing machine learning algorithms that provably help us make better decisions? Join us as a post-doc in the Division of Data Science and AI, Department of Computer Science
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disorder. This project investigates early neural markers of psychosis by integrating multimodal neuroimaging with genetic and transcriptomic data and applying machine-learning approaches to identify
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). The project focuses on developing computational models for cancer risk assessment, integrating multiple types of data and risk factors. The main objective is to design and apply machine learning and deep
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risk factors. The main objective is to design and apply machine learning and deep learning methods to understand and investigate the functional behavior of gender-specific cancers. The work will include
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materials and technologies. Using advanced computational modeling and machine learning, we seek to elucidate the mechanisms governing the self-assembly of lignin in different solvents and the formation
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at: https://www.umu.se/en/department-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models
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-of-computing-science/ Project description and working tasks The project will develop privacy-aware machine learning (ML) models. We are interested in data driven models for complex data, including temporal data
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fellow devotes most of their time to research. There is the possibility of teaching up to 20%. Requirements Requirements PhD degree in in machine learning, automatic control, system identification, signal
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-Geometric Foundations of Deep Learning or Computer Vision KTH Royal Institute of Technology, School of Engineering Sciences Job description The Department of Mathematics at KTH welcomes applications for a
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description and working tasks The project will develop privacy-aware machine learning (ML) models. We focus on data-driven models for complex and temporal data, including those built from synthetic sources