The University of Colorado Comprehensive Cancer Center (UCCC) is seeking a PhD-level Bioinformatics researcher that specializes in analysis of –omics data in the cancer research space and has a desire to help train postdoctoral research scientists. The ideal candidate will have a strong track record, as demonstrated through publications, in the analysis and interpretation of -omics data generated from cancer samples. Additionally, a track record of algorithm development with strong programming skills, primarily using R. Finally, the ideal candidate will have a desire to train exceptional postdoctoral researchers to both develop Bioinformatics analysis plans around their research topics and then to execute on those plans. This position is highly collaborative with opportunities to both develop independent research and collaborate with UCCC members, who are top tier research scientists. Roughly half of time for this position will be dedicated to research and working on projects through the UCCC Biostatistics and Bioinformatics shared resource. The remaining half of the time for this position will be dedicated to education, mentoring and training. This position offers a unique opportunity for a highly qualified individual to gain experience in both research and education at a top tier medical research campus. This position will also offer the opportunity to develop independence of research through individual and collaborative grant funding.
The UCCC is the top-ranked NCI-designated comprehensive cancer center in the mountain region of the United States. The UCCC has a membership of 230 faculty that span research from basic cancer biology to epidemiology and cancer prevention. The University has made major investments into centralizing patient medical records for research purposes, brining in cutting edge single cell technologies at the protein, DNA, and RNA level (10x Genomics, VECTRA, MIBI, mass spectrometry, high content microscopy), made major investments in to building research programs in Immuno-oncology, data analytics, RNA biosciences, and Fibrosis. The UCCC is committed to developing cutting edge basic research and translating these findings into the clinic. With 15 shared resources, including Genomics, Functional Genomics, Proteomics/Metabolomics and Biostatistics and Bioinformatics, The UCCC offers cutting edge resources to its members.
This position will be fully supported through the UCCC with generous salary compensation commensurate with the qualifications of the individual.
· Analysis of -omics datasets through the Biostatistics and Bioinformatics shared resource, with a focus on next generation sequencing data, particularly for whole genome and whole exome data.
· Develop collaborative relationships with UCCC members
· Development and implementation of algorithms for the analysis of -omics data
· Writing and reviewing of grants and manuscripts
· Development and implementation of a training program for roughly 6 postdoc researchers with limited Bioinformatics training per year
· A PhD in Bioinformatics/Biostatistics/Computational Biology or related fields.
· Demonstrated track record in the Bioinformatics/Computational Biology/Mathematical Modeling space through peer-reviewed publications.
· A deep understanding of NGS technologies and development of the pipelines to analyze the associated data.
· A demonstrable ability to assemble an analysis pipeline for whole genome and whole exome sequencing data
· Code-base that is managed through a software versioning system
· A track record in cancer research
· A desire and track record for training others in Bioinformatics analysis
· Programming in R along with another language
· Excellent communication and interpersonal skills
· A desire to work collaboratively with multiple researchers in the cancer space
· Interest in developing new algorithms for new technologies, such as multiplexed ion beam imaging and/or single cell technologies
· Experience with developing and writing grant applications or parts of grant applications around Bioinformatics analysis
· Previously developed and taught a Bioinformatics training course
· A proven track record in analysis of other –omic datasets
· Application and development of deep learning methods