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Field
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Demonstrated creativity, independence, and scientific excellence Experience working with large-scale data, modeling, or AI/ML methods is an advantage If you fulfill all the requirements, you may be eligible
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their immunogenicity; Develop analysis strategies for large data sets and correlation matrix using programming languages (R, Python, SQL, etc.); Scientific supervision of undergraduate and doctoral
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from sending a photo as part of the application. Information on how we protect your data can be found on our website The IfL advocates professional equality for all genders. We value diversity and
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, in Africa and Europe. Requirements include a university degree (master's or equivalent) in archaeometry, mineralogy, or materials science, as well as the motivation to develop fundamental new data
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to work on fundamental new data relevant to archaeological research and a willingness to work in a team. A further requirement is a willingness to undertake several weeks of fieldwork (collection of raw
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optical communication networks and systems, as well as machine learning, computer vision and compressing digital videos. The Applied Machine Learning (AML) group is part of the Department for Artificial
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and methodological skills with a focus on quantitative data analysis (e.g., econometrics, statistics, machine learning) a high motivation and the ability to work independently with a strong team
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sorting and data analysis Generation of single cell libraries with 10X Genomics, Singleron and BD Rhapsody platforms for ATAC, transcriptomics and multiomics assays Quality control of cells and nuclei (e.g
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train deep learning models (CNNs, GNNs) for creating 2D floor plans Prepare data: Build our data pipelines – from reading architectural drawings to preprocessing and augmentation, all the way to labeling
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household panel survey that is planned to be implemented in three different sites in Sub-Saharan Africa. We will hence mainly but not exclusively rely on quantitative data and methods. Applications