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of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
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(applications by email will not be considered). Please include a single pdf in your application that combines the following elements: • a detailed CV (including publication list if applicable) • a one-page
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ourselves on fostering an inclusive environment where diverse perspectives thrive, and people can feel free to disagree. Utilizing cutting-edge techniques like single-cell analysis, CRISPR technology, stem
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Post-doc eligibility criteria Required Skills Minimum 3 years hands on experience with complex flow cytometry is required, including panel design, testing, troubleshooting and analysis Experience in
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spectral flow cytometry and microscopy (FELASA certificate required). Experience with -omic approaches and computational tools for data analysis is desirable. Solid publication record in peer-reviewed
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Excellent communication skills in English Desirable but not required Skills in phytoplankton genetic engineering Hands-on experience with high resolution mass spectrometers and/or data analysis Programming
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spectral flow cytometry and microscopy (FELASA certificate required). Experience with -omic approaches and computational tools for data analysis is desirable. Solid publication record in peer-reviewed
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computing clusters and analysis of transcriptomics and genomics datasets. Desirable Requirements Experience in single-cell and spatial OMICS data analysis. Development of ShinyApps and
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to the development of a radically new method for 3D reconstruction of nanomaterial scans. The technique will be dedicated to the analysis of semiconductor samples in close collaboration with imec Belgium (https
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on cancer metastasis and novel metabolic pathways. We exploit mouse models, genetic engineering, metabolomics and single cell & spatial multi-omics analysis to gain groundbreaking insights into metabolism as