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application! Data-driven life science (DDLS) uses data, computational methods and artificial intelligence to study biological systems and processes at all levels, from molecular structures and cellular
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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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, vulnerabilities in isolated systems are analyzed to pioneer new research methods and train both students and professionals. More about cybersecurity research at LiU: https://liu.se/en/research/cybersecurity
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-year project carried out in close collaboration with our industry partner. The goal is to develop methods for an ML-based decision support system for monitoring and fault diagnosis of gas turbines
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communication networks using AI-powered methods. We will advance the research front in defending future generation networks by: prevention of cyberthreats through anticipating and mitigating them, accurate
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analysis focuses on the development of high-order accurate, provably stable methods that produce reliable approximate solutions to difficult nonlinear problems. These discretisation techniques include, but
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application! We are looking for a PhD student in biomedical engineering with a focus on deep learning for medical images Your work assignments The position focuses on developing methods for federated learning
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look forward to receiving your application! Do you have a background in machine learning and interested in telecommunications? You have a chance to contribute to development of sensing methods for new
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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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methods to provide explainable outputs from AI models in presence of attacks on the models or data, and scalable methods that move beyond feature attribution aiming for root cause analysis and decision