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19.07.2022, Academic staff The Machine Learning and Information Processing group at TUM works in the intersection of machine learning and signal/information processing with a current focus on deep
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to apply. Applicants with disabilities are treated with preference given comparable qualification. further information: www.bio.nat.tum.de/cell/home/ The position is suitable for disabled persons. Disabled
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particularly encouraged to apply. Applicants with disabilities are treated with preference given comparable qualification. further information: www.bio.nat.tum.de/cell/home/ The position is suitable for disabled
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generation, sequence analysis, and data management Rapid Evaluation of newly emerging computational tools relevant for protein design Implementation and optimization of experimental workflows for rapid protein
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for disabled persons. Disabled applicants will be given preference in case of generally equivalent suitability, aptitude and professional performance. Data Protection Information: When you apply for a position
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-throughput data acquisition Quality control, method validation, and standardization of analytical procedures Collaboration with research groups within BioSysteM and exchange with your Technology hub colleagues
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molecular pathways of cancer by analyzing and integrating multi-omics and clinical data. Key Responsibilities Analyze and interpret multi-omics datasets, including genomic, transcriptomic, and clinical data
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(e.g. RNAi, CRISPR/Cas9, small-molecules). In this context, we also develop new computational tools for automated analysis and data visualization. These include algorithms and software applications
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cells of individuals at high risk of developing type 2 diabetes and its complications. Tasks Perform end-to-end data analysis, including quality control, data integration, and inference of regulatory
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quantitative, real-time multimodal mesoscopic and microscopic imaging. • Contribute to real-time software development for system control, data acquisition, multimodal image reconstruction, and quantitative