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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
insights through iterative, hypothesis-driven computational analysis. 2) Characterizing the landscape of structural mutations—including intragenic rearrangements (IGRs)—across cancer types and modeling
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, bioinformatics, systems biology, bioengineering, chemical engineering, or a related discipline Knowledge and experience in the analysis of metagenomics, untargeted metabolomics, or other biological high-throughput
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of non-viral gene transfer systems Production and validation of gene vectors in vitro Establishment of protocols for T-cell manipulation Independent planning, execution, and analysis of experiments
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methodologies: optogenetics, calcium imaging, viral tracing, tissue clearing, murine behavioral phenotyping, machine-learning behavioral analysis Familiarity with programming languages (e.g. R, Python) and an
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record in peer-reviewed international journals Experience with remote sensing, LiDAR, and GIS applications Programming skills in Python Background in LiDAR point-cloud analysis and vegetation structure
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mouse models (handling, treatment, phenotyping) ⦁ Experience with stem cell research and microscopy ⦁ Strong analytical skills and familiarity with data analysis tools ⦁ Highly motivated, and
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related fields. Skilled in data analysis and interpretation; experience with genomic analysis, automation, or computational tools desirable. Proven ability to work independently, think creatively, and solve
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single cells make decisions during differentiation, in particular during development. Building on Bonsai, a Bayesian framework that leverages tree structures for distortion-free exploratory analysis
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custom data analysis workflows Good English knowledge (approximately equivalent to CEFR level B1+B2) A collaborative mindset Preferred Qualifications and Skills Hands-on experience with single
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, or biophysical simulations. Demonstrated interest in biological systems, prior experience in biological modeling and in transcriptomic data analysis. Proficiency in programming (e.g., Python, R) and familiarity