Now hiring a Post-doctoral Research Fellow of Cancer Data Science in the Translational Bioinformatics and Cancer Systems Biology Laboratory of Dr. Aik Choon Tan (tanlab.org ) at the Huntsman Cancer Institute, University of Utah. We are looking for a talented, energetic and highly motivated individual to join our lab to contribute to the vision of a cancer-free frontier! This position will be involved in innovative cancer genomic projects linking bench to bedside. The research fellow will develop computational and machine learning algorithms to integrate multi-omics data sets (e.g. imaging, genomics, transcriptomics, single cells, spatial transcriptomics, clinical data) for decoding tumor ecosystem, identifying predictive biomarkers for therapeutics and drug combinations. The research fellow will have the opportunity to be trained by computational and experimental mentors, and will be supervised by Dr. Aik Choon Tan. He/she will have the opportunity to collaborate with cutting-edge oncology research groups at the Huntsman Cancer Institute and work on translational research projects. If you have the vision, passion, and dedication to contribute to our mission, then we have a place for you!
Position Highlights:
• Opportunity to work in exciting translational cancer research projects in a multi-disciplinary team science setting.
• Participate in advanced cancer genomics and cancer systems biology research projects.
• Outstanding mentorship from expert faculty with wide-ranging funded research programs.
• Work at the intersection of data science and cancer research.
• Translating bioinformatics research in clinical trials.
The Ideal Candidate:
• The successful applicant will have completed (or be close to completing) a Ph.D. or M.D./Ph.D. in an applied quantitative discipline. This includes, but is not limited to, Computer Science, Informatics, Biostatistics, Population/Evolutionary Genomics, Pharmacology or Applied Mathematics.
• The applicant should have a strong interest in either basic or translational research. A track record of applying computational and/or statistical models to solve biological problems is desired.
• Experience in common programming languages is needed (e.g., R, Python, C, Java, Perl).
• Experience in machine learning or deep learning methods.
• Preference will be given to applicants with strong evidence of productivity, creativity, and self-motivation.
• A track record of conference poster and/or oral presentations, and first author peer-reviewed publications, is expected.
• The ability to independently gain new skills and solve problems.
• Effective communication skills and fluency in written and spoken English.
Responsibilities:
• Design, develop and deploy computational and statistical learning methods in integrating cancer genomics data.
• Works closely with the Principal Investigator to manage, analyze and interpret data.
• Contribute to peer-reviewed publications.
• Present results in conferences and meetings.
• Communicate effectively in team science settings.
• Contribute to rigor and reproducible data science research.
Huntsman Cancer Institute (HCI) is an Equal Opportunity Employer committed to hiring individuals whose merit and experience promote a diverse, inclusive, anti-racist workforce and culture.
Each employee has a unique background and life experience. We believe that maximizing diversity fuels the success of our organization. In your cover letter or during your interview, we invite you to share how your background, beliefs, and experience will prepare you to be effective in working in an environment that values diversity and is committed to equity, diversity, and inclusion.
Learn more about HCI’s commitments at huntsmancancer.org/edi and/or contact HCI’s Office of Equity, Diversity, and Inclusion.
• MD or PhD in Computer Science, Bioinformatics, Informatics, Biostatistics, Computational Biology, Biomedical Informatics, Statistics, Population/Evolutionary Genomics, Genomics, Cancer Biology, Pharmacology or Applied Mathematics.
• Should have at least one first authorship publication in peer-reviewed journal.
Online utah.peopleadmin.com/postings/143407