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Field
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advanced data analysis. Most of the research will be carried out in the battery research laboratories, which are equipped with state-of-the-art facilities for materials synthesis, characterization (XPS
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proteomics, advanced image analysis, and computational approaches to investigate molecular and cellular heterogeneity and to integrate spatial molecular information with histopathological and clinical data. We
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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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spatial omics, image analysis, and computational analysis of complex biological data. Work duties The main duties involved in a post-doctoral position is to conduct research. Teaching may also be included
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spatial transcriptomic data. A demonstrated interest in data visualization and large-scale data analysis is highly desirable. The ideal candidate will have a keen interest in understanding complex
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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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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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-edge detectors and provide data processing, visualisation and analysis solutions that enable researchers to rapidly interpret results from X-ray scattering, imaging and spectroscopy experiments. Our