Bioinformatics.org
[Regeneron]
Not logged in
  • Log in
  • Bioinformatics.org
    Membership (42221+) Group hosting [?] Wiki
    Franklin Award
    Sponsorships

    Careers
    About bioinformatics
    Bioinformatics training
    Bioinformatics jobs

    Research
    All information groups
    Online databases Online analysis tools Online education tools More tools

    Development
    All software groups
    FTP repository
    SVN & CVS repositories [?]
    Mailing lists

    Forums
    News & Commentary
  • Submit
  • Archives
  • Subscribe

  • Jobs Forum
    (Career Center)
  • Submit
  • Archives
  • Subscribe
  • Career Center - Message forums

    Opportunity: Postdoctoral Scholar -- Computational Genomics -- Deep Learning for Genome Annotation @ University of Connecticut -- Storrs, CT (US)
    Submitted by Jill Wegrzyn; posted on Thursday, September 17, 2020

    BACKGROUND

    The Plant Computational Genomics laboratory at the University of Connecticut (Storrs, CT) has an opening for a Postdoctoral Scholar.

    The Plant Computational Genomics Lab (plantcompgenomics.com) is part of the Department of Ecology and Evolutionary Biology and is closely linked with the Institute for Systems Genomics. Our lab is highly collaborative, multi-disciplinary, and inclusive. Diversity, creativity, integrity, and ambition are values we affirm. We are committed to open and inclusive science. This includes transparency in data acquisition, analysis, and code.

    RESPONSIBILITIES

    The successful candidate will work as part of an interdisciplinary team. They will also work closely with existing genome assembly and annotation projects that represent tremendous organismal diversity. EASEL will be integrated into large-scale international efforts to annotate genomes. As such, the scholar will provide high quality genome annotations for a large network of collaborators through the development cycle. The candidate should have experience with genomic/transcriptomic data, machine learning, and software development. Experience with genome annotation is ideal. The successful candidate will also be involved in training end users and leading publications.

    This individual will take a lead role in the development of EASEL (Efficient, Accurate, Scalable Eukaryotic modeLs), an integrated and accessible deep learning framework to improve the annotation of eukaryotic genomes. This software will provide an efficient and flexible approach, encompassing the full workflow from repeat identification through gene model annotation. EASEL will improve the accuracy of evidence-based and ab initio-derived gene models for organisms with limited or extensive external genomic evidence. The final product will be implemented as stand-alone software, within open-source community platforms, and compatible with HPC systems. We are committed to developing mechanisms for cross-platform data/application sharing that builds on existing efforts with Galaxy (usegalaxy.org), the Tripal platform (tripal.info), and cloud-based HPC.

    REQUIREMENTS

    The qualified applicant will have a PhD degree in Bioinformatics, Evolutionary Biology, Computational Biology, Genetics, or a related field. Biology/Bioinformatics experience is essential and previous experience with software development is desired. The applicant should have experience with Linux/Unix, scripting languages (Python), R, and machine learning. The position is renewable after the first year, for up to three years. As a result of the pandemic, the successful candidate can start work remotely.

    TERMS

    Duration: Full-time

    LOCALE

    University of Connecticut, Storrs, CT, USA

    HOW TO APPLY

    Interested applicants, Please send the following THREE documents: cover letter, research statement, and CV to: Jill Wegrzyn at jill.wegrzyn[at]uconn.edu

    DEADLINE

    Applications will be accepted until October 9th, 2020.

    Start Date: Negotiable

    Expanded view | Monitor forum | Save place

    Start a new thread:
    You have to be logged in to post a reply.

     

    Copyright © 2020 · Scilico, LLC