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changes and established markers for Alzheimer's disease. The project may also include machine learning methods to estimate individuals' biological age. The project is based on existing data from a prominent
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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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using hybrid models combining mechanistic, GenAI, and machine learning approaches. You’ll contribute to building disease-specific Digital Twins using large-scale single-cell multi-omics datasets
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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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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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, 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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demonstrates excellent scientific, interpersonal, and communication skills. Technical proficiency, scientific creativity, collaboration with others and independent thought. To learn more and apply, please visit
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(EHR), health information exchanges, and data analysis software. Experience with health IT innovation, including working with artificial intelligence, machine learning, telemedicine, or mobile health
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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
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interest in methods development Experience in one or more of the following areas: algorithms development, transcriptome analysis, RNA modifications, statistics, machine learning, long read RNA-Sequencing