Post-Doctoral Fellow

Job Summary:

The Pividori Lab (pivlab.org; PI: Dr. Milton Pividori) designs and implements machine-learning methods that are applied to human disease with a systems biology perspective. The lab's goal is to advance precision medicine by developing a comprehensive, multi-omics approach to enhance our molecular understanding of complex diseases and their therapeutic modalities. As a computational research laboratory, we collaborate with experts in different disease domains to translate our findings to the clinic. Our group deeply cares about open science, and we use the latest software development technologies that enable reproducible research and easy adoption of our tools.

This full-time (1.0 FTE) Postdoctoral Fellow position is funded through an NIH INCLUDE (INvestigation of Co-occurring conditions across the Lifespan to Understand Down syndromE) R01 grant, in collaboration with Dr. James Costello (Department of Pharmacology) and Dr. Casey Greene in the Department of Biomedical Informatics. The position will be primarily appointed in the Pividori Lab and will work jointly with the Costello and Greene labs to develop novel computational, statistical, and machine learning methods to integrate diverse multi-omics datasets and disentangle the link between the triplication of chromosome 21 and the variable spectrum of co-occurring conditions that arise in Down syndrome.

We are seeking candidates with a strong background in the design and implementation of novel machine learning models and their application in biomedical research. Experience with multi-omics data analysis, statistical/computational methods development, and disease-focused research are highly desirable. Individuals from diverse backgrounds are strongly encouraged to apply.

Key Responsibilities:

• Research:
  • Design and planning of research projects and literature review.
  • Design innovative machine learning and statistical models to analyze and integrate large multi-omics datasets (such as genomics, transcriptomics, proteomics, prior knowledge/pathways, drug data) to extract testable biological hypotheses about Down syndrome and its co-occurring conditions.
  • Apply these methods to disentangle the link between the triplication of chromosome 21 and the molecular mechanisms driving co-occurring conditions in Down syndrome.
  • Develop collaborative research projects with the team of faculty, postdocs, and graduate students across the Pividori, Costello, and Greene labs.
  • Write software and analytical workflows to carry out specific experiments.
  • Analyze and interpret research results.
  • Maintain effective communication with the PI, other team members, and local and international collaborators.
  • Keep an organized record of research experiments and results on GitHub.
  • Write Python/R packages that are easy to install and use.
• Communication:
  • Present research in progress at lab meetings, project meetings, and one-to-one meetings with the PI.
  • Prepare manuscripts for publication in scientific journals and blog posts for non-experts.
  • Submit abstracts and present results at local and (inter)national conferences.
  • Actively pursue fellowship awards and assist the PI in grant writing.
• Mentoring and Collaboration:
  • Mentor and train graduate and undergraduate students in the group and across the collaborating labs.
  • Actively pursue computational, experimental, and clinical collaborations.
• Career development:
  • Attend career development seminars and networking events.

* Complete an Individualized Development Plan (IDP) to be discussed with the PI annually.

Work Location:

Hybrid - this role is eligible for a hybrid schedule of #3 days per week on campus and as needed for in-person meetings.

Why Join Us:

The Department of Biomedical Informatics (DBMI), located in the magnificent new Health Science building with a fabulous view of the Rocky Mountains, provides a fantastic environment for trainees to thrive and pursue their career goals. DBMI has a dedicated team for Grants Development and Scientific Writing, and a Software Engineering Team that can provide invaluable feedback to improve the quality of your code and your programming skills.

This position is highly collaborative: the postdoctoral fellow will gain training experience by working across three groups - the Pividori, Costello, and Greene labs - which together provide expertise in machine learning, computational biology, statistical genetics, and multi-omics integration. The position will also have opportunities to engage with the Linda Crnic Institute for Down Syndrome at CU Anschutz, a leader in Down syndrome research internationally. With over 100 researchers supported through the Crnic Institute, CU Anschutz is an active and engaging research environment for Down syndrome research.

The PI is committed to providing personalized support according to the candidate's research and career goals, including the exploration of new research ideas and collaborations. With his experience in grants writing to transition to an independent position in academia (NIH K99/R00), the PI will provide guidance and feedback into this direction if aligned with the candidate's goals. While this position requires a biomedical computational background, applicants are highly encouraged to apply even if they don't have all the preferred qualifications, since it is understood that many skills will be learned on the job, including software development, computing environments, genetic studies, or multi-omics data integration and analysis. The lab works in a hybrid in-person/remote setting.

Why work for the University?

The University of Colorado offers a comprehensive benefits package. To see what benefits are available for Post-Doctoral Fellows, please visit:

* Payroll & Benefits Orientation for Post-Doctoral Fellows | University of Colorado (cu.edu)

* benefits guide cover-post-doc-2024 (cu.edu)

Qualifications:

Applicants must meet minimum qualifications at the time of hire.

• Graduation from an accredited college or university with a PhD in a relevant science discipline, such as computational biology, bioinformatics, biomedical informatics, computer science, genetics, biostatistics, applied mathematics, physics, engineering, or related fields.
• The successful completion of a research project as evidenced by at least one first-authored and published manuscript.

• Experience with handling large, multi-omics datasets.
• Experience with cloud computing (Amazon EC2 and/or Google Cloud).
• Experience with known packages for machine learning, such as scikit-learn or others.
• Experience with deep learning techniques and software packages such as PyTorch or similar.
• A published example of a developed algorithm, pipeline, or database with application to multi-omics or image data (e.g., a Python package on PyPI or an R package on CRAN/Bioconductor).
• Proven experience in disease research, with a track record in Down syndrome research as a strong bonus.
• Experience in statistical genetics methods, such as genome-wide association studies (GWAS), transcriptome-wide association studies (TWAS), disease risk prediction (polygenic risk scores), and functional characterization.
• A track record of contributions to proposals for research funding.
• Proven experience in methods development for multi-omics data analysis.
• Advanced programming skills in Python and/or R with version control (Git) and attributable contributions to source code, demonstrated through an active GitHub account or equivalent.

• Good english language communication skills corroborated by peer-reviewed first-author publications and oral presentations at (inter)national conferences.
• A demonstrated ability to work collaboratively on multiple projects simultaneously.
• Excellent time management and organizational skills.
• Interest in developing new analytical workflows for emerging technologies (e.g., single-cell technologies or multiplexed ion beam imaging).
• Demonstrated ability to train researchers in bioinformatics skills and techniques.
• Experience researching Down syndrome-related questions.

How to Apply:

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. Three 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: DBMI.HR

Screening of Applications Begins:

Immediately and continues until position is filled. For best consideration, apply by June 22, 2026.

Anticipated Pay Range:

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

$73,000

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

Equal Employment Opportunity Statement:

The University of Colorado (CU) is an Equal Opportunity Employer and complies with all applicable federal, state, and local laws governing nondiscrimination in employment. We are committed to creating a workplace where all individuals are treated with respect and dignity, and we encourage individuals from all backgrounds to apply, including protected veterans and individuals with disabilities.

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:

CU Anschutz strongly encourages vaccination against the COVID-19 virus and other vaccine preventable diseases. If you work, visit, or volunteer in healthcare facilities or clinics operated by our affiliated hospital or clinical partners or by CU Anschutz, you will be required to comply with the vaccination and medical surveillance policies of the facilities or clinics where you work, visit, or volunteer, respectively. In addition, if you work in certain research areas or perform certain safety sensitive job duties, you must enroll in the occupational health medical surveillance program.