Research Scientist in Biochemistry & Molecular Genetics

The Mohi lab in the Department of Biochemistry and Molecular Genetics at the University of Virginia is seeking a highly motivated Research Scientist to study the mechanisms of initiation, progression and drug-resistance of cancer. The projects will involve a variety of biochemical, cell biology and immunological techniques, and RNA-seq and bioinformatic analyses. We are looking for sincere and motivated individuals with scientific curiosity.

General Faculty Members whose primary responsibilities include teaching, research, professional practice, or clinical service without encompassing the full scope of responsibilities expected from tenure-track faculty positions (e.g., an academic general faculty member could have primary responsibilities for research with minimal or no responsibility for classroom instruction, or have primary responsibilities for teaching and/or clinical practice without research obligations).

The successful candidate will:

• In this role, the candidate will utilize cellular and animal models to identify and validate new therapeutic targets in blood and breast cancers and perform pre-clinical translational studies. The candidate is expected to work independently and ethically, document all research data appropriately, submit research reports periodically, assist undergraduate and graduate students in the lab, apply for independent research funding where applicable, present research findings in scientific meetings, and publish data in qualified journals..
  
• In addition to the above job responsibilities, other duties may be assigned.
  

MINIMUM REQUIREMENTS:
Candidates should have a PhD or MD/PhD degree in Biochemistry, molecular biology, cell biology, immunology or related field with at least 3 years research experience in an academic institution. Candidates should have a proven track record in research with first author publications in reputed journals. Experience in working with mouse models and flow cytometry, and knowledge in bioinformatic analysis is preferred.

PHYSICAL DEMANDS
This is primarily a sedentary job involving extensive use of desktop computers. The job does occasionally require traveling some distance to attend meetings, and programs.

The University will perform background checks on all new hires prior to employment.

To Apply:

Please apply through the UVA job board , and search for R0065657.

Internal applicants must apply through their UVA Workday profile by searching 'Find Jobs' and search for R0065657.

Complete an application online with the following documents:

• CV
  
• Cover letter
  

Upload all materials into the resume submission field, multiple documents can be submitted into this one field. Alternatively, merge all documents into one PDF for submission. Applications that do not contain all required documents will not receive full consideration.

References will be completed via UVA’s standardized process Skill Survey. A total of five references will be requested via Skill Survey during the final phase of the interview process.

For questions about the application process, please contact Michelle Johnson , Recruiter.

For more information about UVA and the Charlottesville community please see http://www.virginia.edu/life/charlottesville and https://embarkcva.com/ .

The University of Virginia, i ncluding the UVA Health System which represents the UVA Medical Center, Schools of Medicine and Nursing, UVA Physician’s Group and the Claude Moore Health Sciences Library, are fundamentally committed to the diversity of our faculty and staff. We believe diversity is excellence expressing itself through every person's perspectives and lived experiences. We are equal opportunity and affirmative action employers. All qualified applicants will receive consideration for employment without regard to age, color, disability, gender identity or expression, marital status, national or ethnic origin, political affiliation, race, religion, sex, pregnancy, sexual orientation, veteran or military status, and family medical or genetic information.