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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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industry. You will continuously characterise NC batches for a shared sample bank, build a reference database and use AI-supported data analysis to compare qualities and identify deviations. The work is
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simulation and AI-supported data analysis are central tools. The work is carried out at the Department of Fibre and Polymer Technology and in collaboration with FOI and industrial partners. Qualifications
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), statistical analysis of LHC data or beyond-the-Standard-Model phenomenology, is meriting. Experience with large-scale training on GPU and HPC systems, with design of experiments and active learning, with open
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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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samples, lack of training data and sample variability. In this project we aim to develop AI/ML workflows for improved quantitative analysis of LNPs. Your responsibilities will include optimisation of data
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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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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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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