We are searching for a qualified post-doctoral fellow to join our growing laboratory, whose principal investigator is an NIH-funded researcher. This individual will conduct interdisciplinary and highly collaborative research in ovarian cancer to predict clinically relevant tumor features. The researcher will work on large and diverse molecular datasets, primarily consisting of bulk and single-cell transcriptomic data. The candidate should be proficient in, or highly motivated to learn cancer data science, machine learning, and high throughput sequencing analysis. Successful applicants will need to work both independently and collaboratively, to exhibit excellent skills in scientific communication, and dedicated to scientific reproducibility, replicability, and transparency.
Minimum Qualifications:
Applicants must meet minimum qualifications at the time of hire.
PhD in relevant scientific area such as Computational Biology, Computer Science, or Bioinformatics
Preferred Qualifications:
Prior experience with high-performance computing platform.
Prior experience with high-throughput sequencing data analysis.
Prior experience with applying or building machine learning methods.
Prior experience managing code with a version control system (GitHub, GitLab, etc.)
At least one first author publication or presentation at a national or international conference.
Attributable contributions to source code
Knowledge, Skills and Abilities:
Excellent communication and organizational skills
Practical experience in data cleaning, wrangling, and visualization
Practical experience in deployment of machine learning models on real data
Evidence of working on collaborative and interdisciplinary teams
Demonstrated willingness to give/take constructive criticism and to sustain a supportive and inclusive lab environment
For full consideration, please submit the following document(s):
1) A letter of interest describing relevant job experiences as they relate to listed job qualifications and interest in the position
2) Curriculum vitae / Resume
3) Five professional references including name, address, phone number (mobile number if appropriate), and email address
Applications are accepted electronically ONLY at www.cu.edu/cu-careers.
Questions should be directed to: Olivia Castillo at Olivia.Castillo [at] cuanschutz.edu