Harvard Medical School - Dana-Farber Cancer Institute

The Computational Imaging and Bioinformatics Laboratory (CIBL) is part of Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School (Radiation Oncology & Radiology). This data science laboratory is focussed on the development and application of novel computational approaches of various types of data, in specific from imaging and genomic data. The lab’s research is highly oriented towards real world translation of these findings into clinical diagnostic and therapeutic tools to help improve the lives of patients.

Dana-Farber Cancer Institute, Brigham and Women’s Hospital, and Harvard Medical School are located at the heart of Boston’s Longwood Medical Area, which also includes the Beth Israel Deaconess Medical Center, Children’s Hospital Boston, Joslin Diabetes Center, Mass. College of Pharmacy and Health Sciences, Harvard School of Dentistry, and Harvard School of Public Health. This rich working environment provides ample opportunities for collaboration and scientific exchange within the Harvard and MIT community of clinical, genomics, imaging, computational biology, and machine learning groups.

Positions Available

There are currently positions available in the lab for PhD Students and Postdoctoral Fellows in Computational Imaging & Computational Biology. Therefore, we are looking for outstanding bioinformaticians or (biomedical) engineers. Please apply by sending a resume to Dr. Hugo Aerts.

Publications

Lab members have published in journals such as Nature, Nature Communications, JAMA Oncology, Cancer Research, and Nature Reviews Clinical Oncology. Please find an overview of CIBL publications on the publications page.

Highlights

Animation that provides a high-level overview of Radiomics

Nature-Communications
Recently we published a study in Nature Communications describing a large Radiomics study.

In collaboration with the Haibe-Kains lab and Quackenbush lab, the CIBL published a study in Nature comparing two large pharmacogenomic datasets.

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