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datasets, performing statistical analyses and machine learning approaches to identify biomarkers of treatment responses. The candidate will also develop and implement bioinformatics pipelines in a high
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managing and curating large datasets and with machine learning techniques preferred. Excellent oral and written communication skills and the ability to perform both self-directed and guided research
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or translational research experience Knowledge of machine learning, Bayesian modeling, or statistical method development Ideal Personal Attributes: Independent, proactive, and scientifically curious Detail-oriented
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organoids will be plus. Dry lab: Highly motivated candidates with a PhD/MD degree in bioinformatics, genome science, systems biology, biomedical informatics, computational biology, machine learning, data
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. Preferred Qualifications: Prior experience working with mouse models of cancer is strongly preferred; candidates without prior experience will be considered if willing to learn. Interest in tumor metabolism
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thrombosis and lung injury in Sickle Cell Disease. The prospective candidate will have the opportunity to learn state-of-the-art techniques such as Multi-Photon-Excitation intravital microscopy of the lung and
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independent thinkers, curious and intrinsically motivated, with a passion for basic research. Postdoctoral fellows in the lab bring or learn diverse tools, including: Protein expression and purification
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expertise in machine learning and/or Bayesian models is preferred. This position will involve both methodology development and analysis of multi-omic sequencing data, including spatial transcriptomic data
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, single-cell analysis, and machine/deep learning (preferred but not required). Strong programming and statistical skills (e.g., Python, Perl, R, Bash). Track record of first-author research papers. Strong
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to work within a team environment. Adaptability to a fast-paced, dynamic environment. Multitasking essential. To learn more and apply, please visit: https://careers.dana-farber.org