Job Summary:

Current project funding includes support for work in microbial systems, disentangling the genetic basis of co-occurring conditions, and understanding transcriptomic patterns in high-grade serous ovarian cancer. Methodological avenues include new techniques for integrating large collections of -omic profiles, using knowledge graphs, and/or linking across data types.

Key Responsibilities:

• Participate in the design and initiation of research projects
• Independently carry out literature reviews, analysis, and interpretation of results with guidance from the PI
• Collaborate with the PI, other members of the lab, and local and international collaborators
• Maintain transparent and easy-to-follow documentation of research projects from start to finish, including reproducible and sharable code.

Why Join Us:

The Greene lab works near the intersection of machine learning, public data, and transcriptomics. Recent manuscripts from the group have rigorously probed the performance of deconvolution algorithms, have estimated demographic factors associated with scientists who get quoted in Nature’s news content, have examined the extent to which deep learning is necessary for certain problems, and have reported on software that supports the use of large language models to improve scientific writing with precise attribution. The lab emphasizes rigor in research and has established processes to support this .

Previous postdoctoral trainees from the lab have pursued and secured positions as academic faculty, as well as research positions in both industry and non-profit labs. We are a hybrid in-person/remote group, with flexibility as needed and in agreement with the PI.

The University of Colorado offers a comprehensive benefits package that includes health insurance, life insurance, retirement plans, tuition benefits, ECO pass, paid time off – vacation, sick, and holidays and more. To see what benefits are available, please visit: https://www.cu.edu/employee-services/benefits-wellness .

Diversity and Equity:

The University of Colorado Anschutz Medical Campus is committed to recruiting and supporting a diverse student body, faculty and administrative staff. The university strives to promote a culture of inclusiveness, respect, communication and understanding. We encourage applications from women, ethnic minorities, persons with disabilities, persons within the LGBTQ+ community and all veterans. The University of Colorado is committed to diversity and equality in education and employment.

Qualifications:

Minimum Qualifications:

• PhD in genetics, bioinformatics, computational sciences, data science, machine learning, biostatistics, applied mathematics, computer science, or related fields.

Preferred Qualifications:

• At least 3 years of experience in R, Python, and/or a comparable programming language, preferably in a high-performance cluster computing environment and using version control.
• Experience developing and/or applying machine learning methods to a problem in some domain such as biology, social science, economics, etc. Alternatively, experience working with high-throughput omics, text, or graph-like datasets.
• Proficiency in scientific communication with evidence of first-author manuscript(s) in peer-reviewed journals and oral presentation(s) at national or international conferences.
• Attributable contributions to source code.

Knowledge, Skills and Abilities:

• Practical experience in the Python data wrangling and machine learning stack including pandas, matplotlib, scikit-learn, and/or pytorch and/or practical experience in using R for omics (esp. transcriptome) data analysis and integration.
• Evidence of working in collaborative teams and positively contributing to others’ projects.
• Demonstrated willingness to give/take constructive feedback and to sustain a supportive and inclusive lab environment.

How to Apply:

For full consideration, please submit the following document(s):

Applications are accepted electronically ONLY at www.cu.edu/cu-careers .

Questions should be directed to: Michelle Fiandra at michelle.fiandra@cuanschutz.edu

Screening of Applications Begins:

Immediately and continues until position is filled.

Anticipated Pay Range:

The starting salary range (or hiring range ) for this position has been established as HIRING RANGE:

$70,000

The above salary range (or hiring range ) represents the University’s good faith and reasonable estimate of the range of possible compensation at the time of posting. This position is not eligible for overtime compensation unless it is non-exempt.

Your total compensation goes beyond the number on your paycheck. The University of Colorado provides generous leave, health plans and retirement contributions that add to your bottom line.

Total Compensation Calculator: http://www.cu.edu/node/153125

ADA Statement:

The University will provide reasonable accommodations to applicants with disabilities throughout the employment application process. To request an accommodation pursuant to the Americans with Disabilities Act, please contact the Human Resources ADA Coordinator at hr.adacoordinator@ucdenver.edu ?.

Background Check Statement:

The University of Colorado Anschutz Medical Campus is dedicated to ensuring a safe and secure environment for our faculty, staff, students and visitors. To assist in achieving that goal, we conduct background investigations for all prospective employees.

Vaccination Statement:

The University of Colorado has a requirement for COVID-19 vaccinations and full completion thereof by 9/1/21 or upon start date.  Information regarding this requirement, and exemptions can be found at:

Anschutz: https://www.ucdenver.edu/docs/librariesprovider284/default-document-library/3000-general-admission/3012---covid-19-vaccination-requirement-and-compliance.pdf?sfvrsn=4e9df3ba_2

Unit-Specific Exemptions:

• Anschutz Campus – Accommodations may be granted for medical or religious reasons.

The University of Colorado seeks individuals with demonstrated commitment to creating an inclusive learning and working environment. We value the ability to engage effectively with students, faculty and staff of diverse backgrounds.