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Field
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collaborate across disciplines. Basic programming or data-analysis skills (Python/R) are meriting but not required. After the qualification requirements, great emphasis will be placed on personal skills. Target
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efforts, such as the collection and analysis of biodiversity data. See https://www.youtube.com/watch?v=cIw3PekWDRM for an insight into what we have done previously and what the fieldwork can involve. The
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strong scientific background with relevant expertise in cell and/or molecular biology. Interest in programming, computational biology and statistic towards high-throughput data analysis is considered a
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experimental design, modelling, programming, or multivariate data analysis, experience in process development and pilot-scale trials involving membrane and/or food processing technologies, experience
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through a model-driven approach, i.e. a combination of simulation- and data-driven methods and tools with data analysis and machine learning as an important part. The work builds on established theories and
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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activities such as workshops, participatory design, qualitative data analysis, literature reviews, or academic writing. good communication skills in English, both spoken and written. Assessment criteria
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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the MIDA – Methods for Image Data Analysis – research group at the Department of Information Technology, and will be conducted alongside other researchers at the Centre for Image Analysis who develop
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dilution refrigerators or cryostats for low-temperature characterization. • Data Analysis: Automate measurement routines (Python/MATLAB) and analyze complex datasets to validate the trimming process