Research Associate - Cancer Center - 140757

Research Associate
- Cancer Center

The Moores Cancer Center (MCC) is one of just 57 NCI-designated Comprehensive Cancer Centers in the United States and the only one in San Diego County. As a consortium cancer center, it is a collaborative partnership between the UCSD: encompassing 28 departments, 6 schools (School of Medicine, Skaggs School of Pharmacy and Pharmaceutical Sciences, School of Public Health, Jacobs School of Engineering, School of Biological Sciences & the School of Physical Sciences), UCSD Health oncology hospitals and clinics;

the basic and public health research and outreach of San Diego State University (SDSU), and the basic and translational research of the La Jolla Institute of Immunology (LJI). These various programs and units are all dedicated to fulfilling the Moores Cancer Center's mission of reducing cancer's burden. As such, it ranks among the top centers in the nation conducting the continuum of cancer research, providing advanced patient care, and serving the community through outreach and education programs.

As a top-ranking, future-oriented organization, we offer challenging career opportunities in a fast-paced and innovative environment. Moores Cancer Center follows a progressive philosophy of career-path development for its employees including opportunities for cross-training, professional development, and progressive responsibility.

MCC's mission is to transform cancer care in our catchment area and beyond by driving exceptional scientific discoveries and innovations in prevention, detection, care, and training. MCC will make a global impact on improving health by reducing cancer burden through accelerated discovery and translation, compassionate and interdisciplinary care, education and community engagement, with the foundation of our core values
- Excellence, Innovation and Risk-Taking, Collaboration, Diversity and Service.

Under the supervision of a Principal Investigator, the incumbent will apply a wide variety of experimental research techniques to problems of cell growth and oncogenic transformation related to gastrointestinal cancer. Collaborate with PI on methods to the investigation cell signaling and their respective roles in processes associated with tumor progression. Additionally, the incumbent will provide bioinformatics, statistical, data management and report support for computational analyses and lab research with primary analysis focus in multi-omic analyses.

Perform advanced data analysis and statistical methodologies based on integrative approaches including, e.g., genomic, proteomic, clinical and other types of data. Independently perform routine laboratory experiments, such as immunoblotting, cell viability assays, confocal microscopy, Real-Time PCR analyses and immunofluorescence, cell cycle analysis of in-vitro cell lines. Independently work on data preparation for large scale data analysis and provide support for manuscript publication.

• Theoretical and practical knowledge of cellular, molecular and genetic biology; their techniques and approaches, with demonstrated ability to perform independent scientific inquiry or equivalent experience.
• Theoretical and practical knowledge of cancer research (preferable to have experience with tumor microenvironment, cancer stem cells, and/or drug screening) with demonstrated ability to perform independent scientific inquiry or equivalent experience.
• Thorough knowledge of statistics, applied mathematics, bioinformatics, or related field. Theoretical knowledge of research function in Computational Biology, Genomics, or related scientific discipline. Thorough knowledge of multi-omics terminology including knowledge of cancer genomics and pathway enrichment methods.
• Experience working with DNA/RNA isolation, PCR, plasmid purifications, cell/tissue lysates, immunoblotting, and immunostaining of isolated cells and fixed/frozen tissues.
• Experience with aseptic and sterile techniques. Experience with cell/tissue culture such as performing recombinant engineering and/or selection/isolation of transient and/or stable transgene-expressing cells for phenotypic analyses in downstream studies, including the use of viral vectors for gene delivery.
• Demonstrated experience with data analysis, computer programming and graphing complex and custom statistical programming using statistical software.
• Demonstrated working experience with statistical methods and models incorporating a broad range of study designs and traditional and modern statistical approaches, as well as machine learning techniques.
• Demonstrated experience with multi-omic dataset queries, management, and quality assurance. Demonstrated experience in multi-omics data integration (e.g., epigenomics, proteomics). Familiarity with spatial transcriptomics and single-cell multi-omics approaches.
• Excellent time management and strong demonstrated organizational and multitasking skills including establish priorities, meet multiple and frequently changing deadlines…