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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and
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, Cybersecurity, AI, Machine Learning (ML), Data Science, or another closely related subject, no more than three years before the application deadline; has documented knowledge of AI and ML; has demonstrated
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proven experience, an area that has been strengthened by the national initiative ULF (Development, Learning, Research). Learn more here: https://www.umu.se/en/department-of-creative-studies/research
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data and multimodal datasets combining imaging and molecular measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with
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required to meet the eligibility criteria. Of secondary importance are: Experience with Python and relevant libraries for machine learning, optimization and simulation. Documented expertise in simulation
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measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with modern deep learning frameworks (PyTorch, JAX, or equivalent). Have
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. Required qualifications: PhD in a field such as physics, systems biology, applied mathematics, machine learning, or related fields. Strong programming skills (e.g. Python) and experience with modern ML
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, multi-omics analyses and systematic bioinformatics techniques is a strong advantage. Excellent programming skills (Python/R) and a solid training in AI or machine learning are highly preferred. A strong
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domains such as telecom, defence and cloud. You will join the Machine-Intelligence for Networks and Distributed Systems (MINDS) research group at the Department of Computing and Learning Systems, School
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description Work on EU projects to develop next‑generation transport, emission and health forecasting models by integrating deep learning, xAI, and diverse data sources such as traffic sensors, smart‑card data