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techniques for studying dough and bread characteristics (e.g., rheology and texture analysis) Heat and mass transfer Mixing technology Statistical analysis Personal qualities: Inquisitive and driven
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unable to load from YouTube. Accept cookie and refresh page to watch video, or click here to open video) About the position A new PhD fellowship in path signatures, stochastic analysis, and learning from
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at the interface of engineering, inspection data, and decision support for infrastructure maintenance. Experience with the analysis of sensor data, image databases, or other technical datasets is an advantage
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animal husbandry. The candidate can select where to place the main emphasis of the work and may also explore life-cycle analysis, human exposure, or cost-benefit modelling. You will work with NIBIO, NGI
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. • Experience with in silico molecular docking and molecular dynamics simulations applied to toxicological or biochemical questions. • Experience with qPCR-based gene expression analysis and standard molecular
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in finance or insurance data analysis, time series analysis, financial econometrics, and statistical learning is an advantage. Applicants must be able to work independently and in a structured manner
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of the position. The successful candidate will have a solid theoretical foundation in one or more of the topics: Computational Mechanics, Finite Element Analysis (FEA), Numerical Optimization
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bioinformatic analysis of high-throughput sequencing data (e.g., quality control, alignment, peak calling, differential expression analysis, and integrative multi-omics analysis) is a strong advantage. Candidates
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molecular cell biology (using techniques such as cell culture, CRISPR/Cas9 based genome editing, protein expression and interaction analysis, confocal and live cell microscopy), as well as bioinformatics
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cell culture, CRISPR/Cas9 based genome editing, protein expression and interaction analysis, light microscopy and cancer assays. A solid background in data analysis, bioinformatics and presentation