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School of Engineering Sciences in Chemistry, Biotechnology and Health at KTH Job description Quantitative analysis of lipid nanoparticles (LNPs) using Cryo EM is challenging due to heterogenous
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collection with informal caregivers, healthcare professionals and other stakeholders, register and retrieve data from REDCap. Analyse qualitative data using, for example, reflexive thematic analysis
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(NC). In the project, we aim to establish a reliable analysis package for nitrocellulose. Your role is to develop and validate methods for the key properties of NC – including degree of substitution
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distillation, membrane processes and sorption. You will carry out laboratory and smaller pilot trials on the most promising solutions, perform a techno-economic analysis and propose a greener NC process. Flow
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data analysis and machine learning (e.g. XGBoost), including model interpretation techniques (e.g. SHAP). Very good oral and written proficiency in English. Excellent communication skills, ability
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contamination. We have developed a quantitative LC-MS/MS method for their analysis in different matrices. The method is further developed by including other fermentation-derived metabolites and is used
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research environments in Computational Science, the research and education has a unique breadth, with large activities in areas such as numerical analysis, mathematical modelling, development and analysis
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battery-related hydrometallurgical leaching as a model system, the project combines controlled experiments, real-time monitoring, chemical analysis, and data-driven modelling. The postdoctoral researcher
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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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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