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project establishes a scalable framework for automated atomic-scale image analysis that can be extended to more complex defect types, materials, and computational microscopy applications. Fellowship 2
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, Matlab and/or R. Strong background in statistical analysis, applicable to experimental validation. Previous participation in scientific and/or technological research projects, preferably related to optics
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of bioinformatics tools, analysis of high-throughput sequencing data, and an interest in the field of ancient DNA (please provide details in the motivation letter and curriculum vitae). Workplan and objectives to be
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Intelligence (AI) algorithms, including Machine Learning (ML) and Deep Learning (DL) techniques, for advanced signal analysis. The work will focus on developing methodologies for the detection, extraction
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, with applications ranging from scientific research to medical imaging and marketing analysis. With the ever increasing amount of learning data, these algorithms face computational challenges