Harvard Medical School
Job posting number: #7126617 (Ref:hh-61651BR)
Posted: February 24, 2023
Application Deadline: Open Until Filled
The Sharpe and Liu labs are seeking a highly motivated individual to join our teams in a co-mentored position as an Associate Computational Biologist. This individual will be mentored by both Dr. David Liu, an expert in computational approaches to dissect cancer therapy resistance mechanisms, and Dr. Arlene Sharpe, an expert in T cell biology and cancer immunology. The Liu lab uses statistical and machine learning approaches to integrate clinical and molecular data from patient samples for personalized care in oncology, biomarkers for rational drug development, and new insights into tumor biology. The Sharpe lab uses cutting-edge techniques including CRISPR/Cas9 genome editing, preclinical mouse models, and single-cell RNA sequencing to improve our understanding of T cell costimulatory pathways in health and disease. The individual will have the opportunity to participate in the analysis of both mouse and human genomic datasets, and the identification of novel targets and pathways for cancer immunotherapy and autoimmunity.
The successful candidate should demonstrate initiative and be invested in the success of research projects. He/she will have enthusiasm for taking a hands-on, problem-solving approach, learning on the job, and collaborating with research scientists and associates in an informal collegial work environment imbued with intellectual rigor. He/she should possess an outstanding academic record and strong communication skills, demonstrate innovative and analytical thinking, and enjoy working in an interdisciplinary team. The successful candidate should have the ability to design algorithms, apply statistical methods, and innovate ways to analyze, integrate, visualize, and interpret a wealth of systems data that will directly inform discovery experiments and target advancement projects, requiring the development of core domain knowledge in genetics, molecular biology, immunology, and computational biology. The successful candidate also should demonstrate experience with multi-tasking and have the ability to work independently as well as part of a team, and possess meticulous work habits with careful attention to detail. This work will be central to several major research projects, and co-authorship on high-impact publications is expected.
Sharpe lab website: https://sharpelab.hms.harvard.edu/
Liu lab website: https://connects.catalyst.harvard.edu/Profiles/display/Person/131167
- Bachelor's degree in a biological or quantitative field (molecular biology, immunology, computer science, statistics, physics, mathematics, or related field)
- 3+ years of experience in the area of bioinformatics
Additional Qualifications and Skills
- Background knowledge in computational biology is preferred, but most important are strong computational and quantitative skills
- Strong programming skills and experience in algorithm development (using Python, R, Matlab, etc.)
- Coursework in Statistics
- Excellent oral and written English communication skills
- Ability to solve complex problems
Additional InformationThis is a term position with an end date of July 31, 2025, with the possibility of extension, contingent upon work performance and continued funding to support the position.
This is an in-person position based on our campus in Boston.
The University requires all Harvard community members to be fully vaccinated against COVID-19 and remain up to date with COVID-19 vaccine boosters, as detailed in Harvard’s Vaccine & Booster Requirements. Individuals may claim exemption from the vaccine requirement for medical or religious reasons. More information regarding the University’s COVID vaccination requirement, exemptions, and verification of vaccination status may be found at the University’s “COVID-19 Vaccine Information” webpage: http://www.harvard.edu/coronavirus/covid-19-vaccine-information/.
Please note that we are currently conducting a majority of interviews and onboarding remotely and virtually. We appreciate your understanding.
Harvard University offers an outstanding benefits package including:
- Time Off: 3 - 4 weeks paid vacation, paid holiday break, 12 paid sick days, 12.5 paid holidays, and 3 paid personal days per year.
- Medical/Dental/Vision: We offer a variety of excellent medical plans, dental & vision plans, all coverage begins as of your start date.
- Retirement: University-funded retirement plan with full vesting after 3 years of service.
- Tuition Assistance Program: Competitive tuition assistance program, incredibly affordable classes directly at the Harvard Extension School, and discounted options through participating Harvard grad schools.
- Transportation: Harvard offers a 50% discounted MBTA pass as well as additional options to assist employees in their daily commute.
- Wellness Options: Harvard offers programs and classes at little or no cost, including stress management, massages, nutrition, meditation, and complementary health services.
- Harvard access to athletic facilities, libraries, campus events, and many discounts throughout metro Boston.
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Commitment to Equity, Diversity, Inclusion, and BelongingWe are committed to cultivating an inclusive workplace culture of faculty, staff, and students with diverse backgrounds, styles, abilities, and motivations. We appreciate and leverage the capabilities, insights, and ideas of all individuals. Harvard Medical School Mission and Community Values
EEO StatementWe are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions, or any other characteristic protected by law.
Harvard Medical School strives to cultivate an environment that promotes inclusiveness and collaboration among students, faculty and staff and to create new avenues for discussion that will advance our shared mission to improve the health of people throughout the world.