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Field
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established and emerging bioinformatics, statistical modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics
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depth in some of the following areas (not all are required): Large-scale data analysis and learning analytics methods Experimental or quasi-experimental design; validity and measurement Working with LLMs
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of Biomedical Informatics; designs and develops novel computational tools for biomedical data analysis; performs large-scale analysis using omics data; assists in identifying new biomedical data analysis
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modifications, structural proteomics, or systems-level analysis of molecular and cellular biology. Research may involve large-scale public proteomics datasets, data harmonization, cross-study integration
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05.07.2026, Academic staff Our research combines mathematical modeling, numerical simulation, scientific computing, and data-driven methodologies to improve the predictive capabilities and
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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to join an interdisciplinary project at the interface of single-cell genomics, data integration, and developmental biology. The project focuses on the integration and analysis of multi-modal single-cell
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large GPU clusters on cryoSTEM datasets in the multi-terabyte range. This position plays a pivotal role in supporting ongoing, high-impact research programs within our lab. The successful candidate will
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demonstrable experience in the analysis of large-scale biological datasets, applying statistical modelling and computational approaches to high-dimensional data such as bulk and single-cell sequencing, gene
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foundation models, directly advancing the frontier of computational biology and machine learning. You will also implement parallel systems capable of training such models across large GPU clusters on cryoSTEM