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their publication record and research statement. Preferred skills will include data science methods such as high performance computation, data management skills, and programming in R. Individuals also must be within
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collaborators to integrate multi-omics (genome, transcriptome, proteome, metabolome) and clinical data Maintain high quality of detailed research data recording and analysis Provide mentorship and training
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to write and communicate clearly Preferred Qualifications Experience with high performance computer clusters Experience with the analysis of large datasets Good understanding of immunology Any kind
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investigative teams and collaborators to develop and conduct exposure assessment and statistical data system and analyses Leading and organizing internal research project team meetings (agendas, decisions, action
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projects across the following areas: Spatial and Single-Cell Proteomics in Childhood Cancer Cell-cell communication & cellular fitness in CAR-T & CAR-NK therapy Deep learning & LLMs in mass spectrometry data
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clean lab environment Experience using thermal ionisation mass spectrometry (TIMS) Experience in U-Pb data reduction protocol(s) (e.g., Iolite, Tripoli, UPB_Redux) Capacity to lead projects with
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genomic approach to profile enhancer dependencies. Develop and optimize experimental workflows integrating scRNA-seq, scATAC-seq, and other single-cell omics. Interpret high-dimensional data in
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of structural data obtained by cryo-electron microscopy Producing innovative recombinant thrombolytic proteins. Generate stably transfected cell lines Produce and purify functional recombinant proteins Evaluate
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, and interpreting models, Analyzing genetics data (e.g. GWAS, eQTLs), including predicting variant effects, stratifying patients, identifying desired patients for recall, Designing, synthesizing, and
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members on combinatorial CRISPR screen strategies for target credentialing in cancer models Computational analysis of screen data Design and execution of combinatorial target validation experiments in PDXO