HMS - Postdoctoral Fellow in Biomedical Informatics (DBMI)

Harvard University; Boston (MA); On-site; USD 55,000 - 70,000; Full time

Job summary

Harvard University seeks a Postdoctoral Fellow in Biomedical Informatics to conduct pioneering research in computational pathology. The role involves developing machine learning methods to analyze various types of pathology data.

The candidate will work alongside an interdisciplinary team, contributing to significant research outputs. A strong background in machine learning and programming in Python and R is essential. This position offers a competitive salary and benefits.

Start date is immediate, with the potential for annual renewal of position.

Qualifications

• A Ph.D. in a relevant field is required.
• Experience in machine learning and programming in Python and R.
• Familiarity with deep learning packages is essential.

Responsibilities

• Conduct original research on computational pathology.
• Develop and refine machine learning methods for analyzing data.
• Collaborate with a multidisciplinary team and publish findings.

Skills

Machine learning

Python programming

R programming

Deep learning (TensorFlow/Keras/PyTorch)

Education

Ph.D. in Biomedical Informatics, Computational Biology, Computer Science, or a related field

Job description

HMS - Postdoctoral Fellow in Biomedical Informatics (DBMI)

Title HMS - Postdoctoral Fellow in Biomedical Informatics (DBMI)

School: Harvard Medical School

Department/Area: Biomedical Informatics-Quad

The Department of Biomedical Informatics (DBMI) at Harvard Medical School and the Yu Lab are seeking a Postdoctoral Research Fellow with experience in machine learning and scientific programming. The candidate will work with a multi-disciplinary team of researchers, including bioinformaticians, pathologists, oncologists, and computer scientists, and conduct original research on computational pathology. Digital pathology images contain rich information on complex diseases. The goal of our efforts is to build and apply automated analytical pipelines for various types of pathology data, including histopathology images and multi-omics (e.g., genomics, epigenomics, transcriptomics, and proteomics) information. The postdoctoral fellow will develop and refine machine learning methods for analyzing histopathology, clinical, and multi-omics data, collaborate with our multidisciplinary team, and publish the findings.

Salary and Benefits

This position is salaried and benefits-eligible. Information regarding postdoctoral fellow salary, which is determined by the number of years post-PhD, and benefits can be found at https://postdoc.hms.harvard.edu/guidelines.

With this appointment, you are represented by the Harvard Academic Workers (HAW) – UAW for purposes of collective bargaining and matters affecting your compensation and working conditions.

Basic Qualifications

• A Ph.D. in a Biomedical Informatics, Computational Biology, Computer Science, or a related field is required. Substantial experience in machine learning, Python and R programming, and familiarity with deep learning packages (e.g., TensorFlow, Keras, or PyTorch) are essential.

Additional Qualifications

Strong problem-solving and communication skills are highly desirable for this opportunity.

Expected Dates

• Start date: This position is available immediately
• Duration: This position has strong potential to be renewed on an annual basis.

Contact Information

Faith McDonald
Countway Library
695 Huntington Ave.
Boston, MA 02115

EEO/Non-Discrimination Commitment Statement

Harvard University is committed to equal opportunity and non-discrimination. We seek talent from all parts of society and the world, and we strive to ensure everyone at Harvard thrives. Our differences help our community advance Harvard’s academic purposes.

Harvard has an equal employment opportunity policy that outlines our commitment to prohibiting discrimination on the basis of race, ethnicity, color, national origin, sex, sexual orientation, gender identity, veteran status, religion, disability, or any other characteristic protected by law or identified in the university’s non-discrimination policy. Harvard’s equal employment opportunity policy and non-discrimination policy help all community members participate fully in work and campus life free from harassment and discrimination.