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emphasis on bioinformatic and evolutionary analysis. Qualification requirements In order to be admitted to postgraduate education, the applicant must have the general and specific entry requirements
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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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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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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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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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well as analysis and evaluation of the results of these. Research publications in reputable scientific journals, where the candidate has a lead author position Ability to present and communicate research ideas
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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
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areas, of which at least 30 credits must be at an advanced level. Courses in statistical analysis, quantitative methods, or mathematical models acquired outside these subject areas may also be included
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areas, of which at least 30 credits must be at an advanced level. Courses in statistical analysis, quantitative methods, or mathematical models acquired outside these subject areas may also be included