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Join a research team working on next-generation multimodal AI models for real-world use in healthcare, automotive, and other safety-critical domains. The position offers hands-on work with deep
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. The long-term goal is to enable targeted interventions for the right individuals, based on their lifestyle, disease trajectories, and molecular profiles. To achieve this, we will apply deep learning models
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, and will apply deep learning to integrate the analysis flows. The PhD student will develop the method and apply to numerous in-house samples of environmental sequences, pushing the boundaries of RNA
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learning, deep learning and relevant software framework (R and Python) is highly desired. Very good oral and written communication skills in English are required. Emphasis will also be given on personal
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practical applications. Experience in machine learning and AI, particularly deep learning frameworks such as TensorFlow, and their application in fluid dynamics and heat transfer research. Programming skills
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of MSI advances our understanding of complex brain processes. The prospective PhD candidate collects brain MSI data and develops novel machine learning methods in connection to generative models such as
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, statistical machine learning and deep learning. Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines . About the
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organizational skills, a willingness to learn and deep dive into the topic of the relationship between hormones and the brain, and willing to contribute to a clinical study assessing brain health in patients with
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physics and strongly interacting systems? As a PhD student in theoretical nuclear physics, you will have the opportunity to explore deep questions about the origin and properties of atomic nuclei and the
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, Internet of Things, Systems-of-Systems automation, Machine Learning, Deep Learning, Data Science, Electronic systems design, and sensor systems. Cyber-Physical Systems (CPS) focuses on integrated software