• Research Fellow - Carter Lab (Machine-Learning Comp Bio)

    Location US-MA-Boston
    Job Posted Date 5 months ago(5/16/2018 4:45 PM)
    Job ID
    2018-10989
    Category
    Fellowships
    Type
    full time
    Grade
    41
  • Overview

    Located in Boston and the surrounding communities, Dana-Farber Cancer Institute brings together world renowned clinicians, innovative researchers and dedicated professionals, allies in the common mission of conquering cancer, HIV/AIDS and related diseases. Combining extremely talented people with the best technologies in a genuinely positive environment, we provide compassionate and comprehensive care to patients of all ages; we conduct research that advances treatment; we educate tomorrow's physician/researchers; we reach out to underserved members of our community; and we work with amazing partners, including other Harvard Medical School-affiliated hospitals.

     

    The Carter lab is seeking a talented and highly motivated post-doctoral fellow in machine-learning to analyze our unique multi-omic characterization of human cancer-tissue samples in order to elucidate basic mechanisms of cancer initiation, progression, and mestastasis. The Carter lab has pioneered the application of statistical methods to extract rich data from genomic sequencing of cancer tissue-samples and infer phylogenetic relationships between cancer subpopulations. We collaborate closely with clinical oncologists and genomic technologists across Harvard, MIT, DFCI, MGH, and the Broad Institute in order to build datasets enabling discovery of key genetic alterations driving adverse cancer outcomes. 

         The fellow will be expected to lead the development and implementation of novel statistical machine learning algorithms and produce usable analysis pipelines supporting our mission. The fellow will join a strong and diverse team of clinical, experimental, and quantitative researchers across our collaborating labs. Successful candidates are expected to excel at critical thinking, be quick learners for new analytical approaches, and capable of applying or developing novel computational methods for solving complex problems. The ideal candidate has both a theoretical and practical understanding of either Bayesian statistics or deep-learning techniques and has a proven track-record in areas such as statistics, mathematical modeling, complex networks data analysis, or statistical physics.

       This position is suited to a person who is excited by the prospect of applying their quantitative skills to computational biology with a strong quantitative somatic genetics focus. 

     

    Responsibilities

    • Work with PI to develop machine learning methods for inference and classification of our datasets
    • Writing usable, maintainable, and documented machine learning code
    • Support analysis by biologist colleagues without formal computational training
    • Publish new methods and results in academic journals and conferences
    • Present findings to internal and external multidisciplinary audiences in a clear and cohesive manner
    • Follow relevant scientific literature to ensure use of optimal methods and understand emerging practices across the field, including testing and evaluating newly developed software tools and methods as they become available
    • Acquire and process external datasets relevant to our research
    • Regularly attend and present at lab and project team meetings to ensure continuous communication around methods and tools developed

     

    Qualifications

    • A PhD in computer science, engineering, mathematics, physics, or other quantitative fields with a strong computational emphasis
    • Experience in cancer biology, genetics, or genomics is helpful, but not required provided the applicant has a strong desire to immerse themselves in these fields
    • Expertise in one of Python, R, Julia, or MATLAB
    • Strong demonstrated skill in statistical algorithm development / machine learning / deep learning
    • Experience with probabilistic programming frameworks such as Python pymc3 is desirable
    • Experience with deep learning using TensorFlow, Theano, or Python/keras is desirable
    • Experience working with high performance compute clusters and cloud compute solutions a plus
    • Independent, highly motivated, highly collaborative and works well with others
    • Excellent communication, organization, and time management skills

     

    Dana-Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other groups as protected by law.

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