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, MetaPhlAn, or similar). Proficiency in programming languages for data analysis (e.g., R, Python). Experience with mass spectrometry-based metabolomics approaches, including LC-MS/MS. Experience operating
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partners. The postdoctoral researcher will also contribute to teaching in areas such as Machine Learning, NLP, AI for Education, Explainable AI, and Python-based applied seminars, supporting course
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field is required. - 2+ years experience in a wet lab research setting. Preferred Qualifications: - Familiarity with Matlab, Python or R for data analysis. - Research experience involving neuroscience
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and/or performance. Appointments include conducting research or project management under the direction of a faculty member. Examples of current and past projects can be found at http
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, including languages such as Java, C/C++, C#, Python and JavaScript, as well as web technologies including HTML, CSS and AJAX/JSON-based APIs. Experience with database systems, including relational and non
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. Proficient in Python, with working knowledge of bash and experience using HPC or cluster environments (e.g. SLURM). A pragmatic scientist who combines technical depth with a clear translational, goal‑oriented
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in machine learning and/or advanced analytical methods, experience working with complex or large-scale datasets, and strong programming skills (e.g., Python or R). You will be able to communicate
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software (e.g. ArcGIS, QGIS) and coding environments (e.g. Python or R), collaborating across LUMHR themes, and supporting interdisciplinary research activity. Teaching support may be required, up to a
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cytometry leading to publication in peer-reviewed journals is recommended. Preferred skills include experience with mouse models, bioinformatics skills (R, Python), background in T cell immunology, and a
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expertise in NGS data analysis (WES, RNA-seq). - Experience with R and/or Python for statistical and bioinformatic analyses. - Experience in integrative multi-omics analysis. - Knowledge of biostatistics and