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university, or an equivalent standard from an overseas university, in a relevant subject such as imaging, biology, biochemistry, chemistry, biomedical sciences, pharmaceutical sciences, data analysis
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novel methodology for the analysis of single-cell (multi)-omics data by incorporating existing biological knowledge into machine learning models. You will join a collaborative and internationally-oriented
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and analyze relevant data and information. Develop and use TIMES-based models. Scenario analysis and communicate results from the analyses. Write scientific journal articles both individually and
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carbon properties influence PFAS removal in drinking water treatment. You will combine laboratory experiments, column studies, and data analysis to identify the factors governing PFAS breakthrough
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or computer vision. A critical understanding of AI model development and evaluation, including the capabilities and limitations of generative AI and synthetic data. Experience with image analysis, synthetic
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, as well as learn how to perform robust in silico analysis of data (training will be provided by senior lab members, collaborators, or experts in the Institute core facilities). The candidate will
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these challenges by using piezo-electric and/or optical fiber based sensor system, combined with physics informed data analysis method, exploiting the digital twin model developed by the first PhD project. In
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: Synthesis and functionalization of nanoparticles. Modification and loading of extracellular vesicles. Analysis of data related to the cellular uptake of nanoparticles and extracellular vesicles. Where
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and telephone interviews, focus groups, and so forth; monitor data collection progress; and seek innovative strategies to increase survey response rates. · Conduct statistical analysis
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this project, you will develop innovative image analysis methods to improve image quality and enable advanced quantitative analyses of ^89Zr-immunoPET data, including radiomics and artificial intelligence