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Welcome students!
We are looking for talented and committed individuals who will work with us in CiRA. Our laboratory has two fields, computational biology and wet experiments. Please see research overview for more information.
Please feel free to contact us if you are interested in our research.

Research topics
①Spatio-temporal analysis of cell differentiations to construct fundamental theories
②Design of 3D tissues derived from iPS cells by single-cell analysis
③Development of human cell database and its medical application
④Establishment of drug screening system for cell therapy based on artificial intelligence


  • Graduate students
    Our laboratory hosts graduate students enrolled in Graduate course, Medicine and Medical science, Kyoto University.
    For students who wish to apply for our laboratory, first you must pass the entrance examination. Please review guidelines and important dates regarding the eligibility screening and the admission examinations. (click here.)
    Procedure for acceptance of graduate students at CiRA is here.
  • Job opportunities for foreign researchers
    We have three types of opportunities available. (click here.)
    1. Fellowship Program of Development of Young Researchers
    2. Fellowship Program of Promotion of Internationalization of Research
    3. International Research Exchange Support (for sabbatical leave)
  • Job opportunities for Staff Positions
    We are currently looking for an informatics staff.
    click here
    for more information.
  • Internship programs
    There are CiRA Research Internship Programs:
    Undergraduate and graduate students
    Research themes of former internship students
    ・Estimating the number of human cell types by independent component analysis
    Susumu Sawada
    ・Improvements of 3D reconstruction based on kernel SOM algorithm in mid-gastrula mouse embryo
    Haruka Takaoka
    ・Improving cell type classification using differential expression analysis and module detection methods
    Emma Goune
    ・Improving models of cell type domains with machine learning and scRNA-Seq
    Anna Sappington