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datasets (e.g. scRNA-seq) to derive prior knowledge on AML dormancy Develop and apply integrative data analysis pipelines (e.g. MOFA, Scriabin, LIANA+, COSMOS) for mining and interpreting multi-omic datasets
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lines and cancer tissue samples. Selected candidates will be part of an international team of PhD students and postdocs that works at the forefront of biomedical research. The PhD student will be a member
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degree in (analytical) chemistry, biochemistry, or a related discipline Hands-on experience in mass spectrometry-based proteomics (e.g. DDA, DIA, PRM) Experience with proteomic data analysis Computer
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for transplantation, isolation of cells, treatment with small molecule inhibitors Computational analyses of scSeq data with support of experienced computational researchers Literature research and manuscript writing
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cell sequencing datasets and to combine these data with AI analyses from bone marrow smears. We aim to analyze mechanisms and also to develop new biomarkers for therapy response and resistance. Job-ID
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lines; generation of samples for proteomics analysis Data integration and analysis, including transcriptomic, proteomic and library screen data Target validation by CRISPR-Cas9 knockouts in primary AMLs
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on the same samples Integrate multi-omics data (e.g., using MOFA) to investigate how patient-specific tRNA modification patterns correlate with treatment response and survival Functionally validate candidate
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are especially invited to apply. Persons with severe disabilities will be given preference if they are equally qualified. Information on job advertisements and the collection of personal data is available
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of methodologies, from in-depth behavioral assessments to computer vision, machine learning and neuroimaging techniques, we aim to uncover the complexites of neurodevelopmental disorders. Our
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be given preference if they are equally qualified. Information on job advertisements and the collection of personal data is available at www.uni-heidelberg.de/en/job-market .