Post-Doctoral Fellow

University of Colorado • Aurora, CO

8d • Hybrid

About The Position

The Pividori Lab designs and implements machine-learning methods 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.

Requirements

• 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.
• 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.

Nice To Haves

• 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.
• 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.

Responsibilities

• 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.
• 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.
• Mentor and train graduate and undergraduate students in the group and across the collaborating labs.
• Actively pursue computational, experimental, and clinical collaborations.
• Attend career development seminars and networking events.
• Complete an Individualized Development Plan (IDP) to be discussed with the PI annually.

Benefits

• Generous leave
• Health plans
• Retirement contributions

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree