SimBioSys (http://www.simbiosys.com) is seeking an exceptionally talented systems biologist to work on one of the most important challenges in medicine - improving outcomes in Cancer. SimBioSys is a technology company on a mission to deploy Computational Oncology to transform decision making and patient experience in Cancer Care. By virtualizing cancer, clinicians and patients are empowered with a better understanding of the disease and can assess all available options computationally to truly individualize treatment.
We are seeking candidates who can contribute to the development and implementation of metabolic models of cancer from -omics data (genomics, transcriptomics, and / or proteomics). The work will require the ability to integrate multi-omics data to develop novel models of metabolism in individual patient tumors. Additionally, the ideal candidate for this position will be able to analyze the large-scale data generated at SimBioSys to generate insights into how patient metabolism varies within and across tumors. Above all, ideal candidates should be creative problem-solvers, with a willingness to try a range of approaches to tackle each new challenge.
The position will require you to design and drive projects and work closely with team members to better understand mechanisms of variation in individual patients, often with limited data. Fostering your analytical, biological, and computational skills, this position will provide you real world experience solving problems, while allowing you to contribute to the fight against cancer.
Requirements and Qualifications
- M.S. or Ph.D. degree in Systems Biology, Computational Biology, Bio/biomedical engineering, Bioinformatics, or a related field (strong candidates with a B.S. will be considered)
- Experience with genome-scale metabolic modeling (COBRApy a plus)
- Strong computer skills
- Proficient in Python and/or C++
- Excellent written and verbal communication skills
- Ability to work independently and as part of a team to meet project goals
Desired Qualifications
- Strong background in cancer biology
- Background in ‘Omics (genomics, transcriptomics, etc.)
- Experience in designing, developing, and implementing software