Research Technician 2, Metabolomics, Lombardi Comprehensive Cancer Center - Georgetown University M
Research Technician 2, Metabolomics, Lombardi Comprehensive Cancer Center - Georgetown University Medical Center
Georgetown Lombardi Comprehensive Cancer Center (LCCC) clinicians are dedicated to providing the best professional care, delivered with compassion, for all patients. Lombardi's internationally-renowned physicians treat virtually every type of cancer, including breast, lung, colorectal, prostate, head and neck, and cancers of the blood. Along with cutting-edge cancer care, we offer a program of innovative clinical trials and a full range of supportive care services to improve our patients' health and quality of life.
The Research Assistant strengthens the activities of high through put metabolomics core facility and the center of innovation for metabolomics. Working with Dr. Amrita Cheema, the Research Technician 2 has duties that include but are not limited to:
- Provides a support role and assists with the day to day operations of the core laboratory including sample preparation, laboratory upkeep, autoclaving, ordering, scheduling waste disposal, laboratory compliance of safety procedures, chemical inventory, scheduling instrument operations and maintenance, data backups and reporting ordering and other duties as needed.
- Uses advanced metabolomics tools for the preparation and analysis of metabolomics data.
- Receives work instructions to analyze data, design, and develop methodologies necessary for accurate knowledge discovery.
- Works closely with the research associates in the laboratory and assists them with day to day service related functions.
- Masters of Science in Engineering, Computer Science, or Bioinformatics
- At least 1 year of relevant experience in biochemistry, mass spectrometry and mass spectrometry data analysis
- Relevant experience in sample preparation, laboratory and utilization of Mass Spectrometry equipment
- Basic knowledge of pre- and post-processing of mass spectrometry data
- Preference for experience in analysis of large volume and high dimensional data sets
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