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qualifications Documented experience with data analysis and programming (e.g., Matlab, Python or R). Experience of risk assessment and/or decision analysis Experience of probabilistic methods such as Monte Carlo
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dynamics to cellular metabolism. The student will receive broad training in cell culture, genome engineering, live-cell imaging, biochemical assays, proteomics, and computational data analysis, and will work
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, Scientific image or microscopy data analysis. You should possess excellent analytical skills, a genuine interest in interdisciplinary research, and the ability to work both independently and as part of a team
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analysis, and the development of social science theory and methods. For further questions: For project- or research-related matters, please contact David Sausdal (david.sausdal@soc.lu.se). For administrative
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and/or dynamic approaches to detect them in the code or prevent their execution at runtime. Keywords for this project: code analysis, static analysis, reverse engineering, defense mechanisms
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efficiency, flexibility, and sustainability. Within this research project, Linköping University is collaborating with leading industrial companies to develop digital analysis and decision-support tools
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molecular simulations. Previous hands-on experience in more than one of the following methods is considered an advantage: molecular simulations, Python programming, machine learning, or quantitative analysis
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biochemistry, especially protein purification, and computational image analysis must be acquired before starting PhD project work. As a PhD candidate, you must also be fluent in both oral and written English
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purification, and computational image analysis must be acquired before starting PhD project work. As a PhD candidate, you must also be fluent in both oral and written English. Merits are: Skills and experiences
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for multimodal machine learning, combining large-scale image data with molecular profiling and clinical data. This includes, for instance, research on deep learning-based image analysis and data assimilation