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. Familiarity with programming languages such as MATLAB and Python, and the ability to work with large imaging datasets Excellent communication and presentation skills, with the ability to prepare high-quality
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) or of bioinformatics (programming skills, R or Python, statistics, analysis of complex datasets) Ability to work independently and to take initiative, as well as teamwork skills German language skills at B1 level are
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Solid programming skills, e.g., Python, machine learning frameworks, data analysis tools Experience with social media research or large language models is an advantage Strong analytical thinking and
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) Experience in cellular and molecular biology, including histology and cell culture (required) Familiarity with using R and/or Python for answering biological questions (required) An enthusiastic and friendly
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of image processing methods on biological datasets, with an emphasis on microscopy. Proficiency in at least one programming language (Python, Java, ImageJ macro, R, Matlab) Familiarity with at least one
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of micropollutants Good basics in hydrological and substance flow modelling Experience in statistical data evaluation and programming skills (R, Python, etc.) Language skills Fluency in English and French or German
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analysis of high-dimensional data (e.g., single-cell RNA-seq, phylogenetics, spatial data). Experience in R or Python. Application instructions: Please send a CV and a brief cover letter describing your
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magnetoencephalography (MEG) and behavioral tests Data analysis using Matlab or Python (speech-brain interactions, synchronicity measurements, connectivity measurements between sources) Presentation and publication
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scenarios. Develop and maintain production-quality software (primarily Python) to enable AI-assisted research processes across computational biology. Prototype, benchmark, and iteratively improve agentic
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or functional genomics datasets. Strong programming skills in Python and/or R, with experience in version control (e.g., Git) Familiarity with machine learning frameworks (e.g., scikit-learn, PyTorch, TensorFlow