The Hemberg group, formerly at the Wellcome Sanger Institute (https://www.sanger.ac.uk/group/hemberg-group/) has recently relocated to the Evergrande Center for Immunological Diseases (https://evergrande.hms.harvard.edu/home) at the department of neurology at Brigham and Women's Hospital and the department of immunology at Harvard Medical School in Boston (website under construction). This location offers unique opportunities in terms of scientific collaborations in a highly dynamic and interdisciplinary environment.
The group's research area is computational genomics and although the main focus so far has been methods development for single-cell RNA-seq, we are interested in all areas relevant to quantitative genomics and transcriptomics. Current areas of interest include developing methods for multi-omics data, spatial data, population data, as well as genomic methods for cancer detection and treatment.
Publication highlights include:
Jimmy Tsz Hang Lee, Nikolaos Patikas, Vladimir Yu Kiselev, Martin Hemberg, Fast searches of large collections of single cell data using scfind, Nature Method, 2021.
Guillermo E. Parada, Roberto Munita, Ilias Georgakopoulos-Soares, Hugo Fernandez, Emmanouil Metzakopian, Maria Estela Andres, Eric A. Miska, Martin Hemberg, MicroExonator enables systematic discovery and quantification of microexons across mouse embryonic development, Genome Biology 22:43, 2021.
Vladimir Y Kiselev, Tallulah S Andrews, Martin Hemberg, Challenges for unsupervised clustering of scRNA-seq data, Nature Review Genetics, (1), 2019.
Vladimir Y Kiselev, Andrew Yiu, Martin Hemberg, scmap - a tool projection of single-cell RNA-seq data onto a reference, Nature Methods, 15, p359-362, 2018.
Vladimir Y Kiselev, Kristina Kirschner, Michael T Schaub, Tallulah S Andrews, Andrew Yiu, Tamir Chandra, Kedar N Natarajan, Wolf Reik, Mauricio Barahona, Anthony R Green, Martin Hemberg, SC3-consensus clustering of single-cell RNA-Seq data, Nature Methods, 14, 483-486, 2017.
Please see Google scholar for an up to date publication list: https://scholar.google.com/citations?user=H4jO_DQAAAAJ&hl=en.
We are currently recruiting postdocs, and we are looking for people with an interest in computational or mathematical biology. Those with a background in related areas such as computer science, physics, chemistry, statistics and mathematics are encouraged to apply. The successful candidate will have strong quantitative skills, ideally with a thorough understanding of genomics, statistics and machine learning. Good programming skills along with high motivation are also desired. Candidates with a background in the wet-lab are also welcome to apply as the lab has designated wet-lab space, and we are interested in projects with both computational and experimental components.
This Postdoc's responsibilities will be two be the lead for one or more research projects, either on their own or in collaboration with other. This will for the most part involve data analysis or the development of novel computational methods for analyzing data. This will in turn require reading the literature and speaking to others to ensure that one is at the forefront of research.
The funding available is for an initial appointment over three years and the starting date is flexible. The funding is not tied to a specific project, so candidates are encouraged to propose their own project. The candidate will be expected to carry out research (both independently and as part of a team), participate in group discussions, and write research proposals and manuscripts. Successful candidates should demonstrate a track record of research achievements and have excellent communication skills. Please contact Dr. Hemberg with a cover letter outlining research interests and a CV at email@example.com.
Brigham and Women's Hospital is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, creed, sex, sexual orientation, gender identity, national origin, ancestry, age, veteran status, disability unrelated to job requirements, genetic information, military service, or other protected status.