Post Doctoral.Associate

University of Pittsburgh; Pittsburgh (Allegheny County); On-site; USD 60,000 - 80,000; Full time

Job summary

The University of Pittsburgh is seeking a highly motivated Post Doctoral Associate to contribute to federally funded projects in machine learning, regulatory genomics, single-cell multi-omics, spatial transcriptomics, and precision oncology.

You will apply and develop computational methods, take ownership of research projects, and advance them toward reproducible software, scientific deliverables, and peer-reviewed publications.

Qualifications

• PhD in computational biology, bioinformatics, biostatistics, statistics, mathematics, computer science, or a related quantitative field.
• Strong programming skills in Python and/or R.
• Demonstrated expertise in machine learning, statistics, omics data analysis, or computational biology.
• Ability to translate broad research objectives into specific analyses, timelines, and deliverables with limited supervision.
• Evidence of completing projects through peer-reviewed publications, software releases, or other substantive research outputs.

Responsibilities

• Lead computational projects from study design and data processing through modeling, validation, biological interpretation, and publication.
• Analyze large-scale bulk, single-cell, multi-omic, and spatial datasets.
• Develop and evaluate machine-learning methods for classification, prediction, multimodal integration, and regulatory inference.
• Apply rigorous validation practices, including data partitioning, cross-validation, baseline comparisons, external validation, and assessment of bias and generalizability.
• Develop reproducible, well-documented, version-controlled, and open-source software.
• Prepare publication-quality figures, manuscripts, presentations, and grant reports.
• Collaborate effectively with computational, experimental, and clinical investigators.
• Provide regular, structured progress updates and communicate challenges and delays promptly.
• Meet milestones and deadlines, respond to feedback, and complete revisions and action items.
• Support mentorship of junior researchers, as appropriate.

Skills

Python

R

Machine learning

Statistics

Computational biology

Education

PhD in computational biology or related field

Tools

PyTorch

TensorFlow

High-Performance Computing

Job description

Job Description - Post Doctoral.Associate (26005937)

Post Doctoral.Associate

Med-Biomedical Informatics - Pennsylvania-Pittsburgh - ( 26005937 )

We are seeking a highly motivated and productive postdoctoral researcher to contribute to federally funded projects in machine learning, regulatory genomics, single-cell multi-omics, spatial transcriptomics, and precision oncology. The successful candidate will apply and develop computational methods, take ownership of research projects, and advance them toward reproducible software, scientific deliverables, and peer-reviewed publications.

Key Responsibilities

• Lead computational projects from study design and data processing through modeling, validation, biological interpretation, and publication.
• Analyze large-scale bulk, single-cell, multi-omic, and spatial datasets.
• Develop and evaluate machine-learning methods for classification, prediction, multimodal integration, and regulatory inference.
• Apply rigorous validation practices, including appropriate data partitioning, cross-validation, baseline comparisons, external validation, and assessment of bias and generalizability.
• Develop reproducible, well-documented, version-controlled, and open-source software.
• Prepare publication-quality figures, manuscripts, presentations, and grant reports.
• Collaborate effectively with computational, experimental, and clinical investigators.
• Provide regular, structured progress updates and communicate challenges, absences, or anticipated delays promptly.
• Meet agreed-upon milestones and deadlines, respond constructively to feedback, and complete revisions and action items.
• Support the mentorship of junior researchers, as appropriate.

Required Qualifications

• PhD in computational biology, bioinformatics, biostatistics, statistics, mathematics, computer science, or a related quantitative field.
• Strong programming skills in Python and/or R.
• Demonstrated expertise in machine learning, statistics, omics data analysis, or computational biology.
• Understanding of model selection, regularization, overfitting, data leakage, class imbalance, performance evaluation, and generalization.
• Ability to translate broad research objectives into specific analyses, timelines, and deliverables with limited supervision.
• Demonstrated ability to independently plan, implement, troubleshoot, and complete computational research projects.
• Evidence of completing projects through peer-reviewed publications, software releases, or other substantive research outputs.
• Ability to critically evaluate and justify analytical decisions rather than relying uncritically on existing pipelines or AI-generated outputs.
• Strong scientific writing, communication, collaboration, and project-management skills.
• Demonstrated reliability, accountability, responsiveness to feedback, and ability to follow projects through to completion.

Preferred Qualifications

• Experience with single-cell, multi-omic, spatial transcriptomic, proteomic, or cancer genomic data.
• Experience with deep learning, graph neural networks, attention-based models, multimodal learning, or regulatory network inference.
• Familiarity with PyTorch or TensorFlow, high-performance computing, and reproducible workflow development.
• A record of leading computational projects or first-author manuscripts from analysis through publication.

Research Integrity and Data Responsibility

The successful candidate must maintain accurate and reproducible research records; protect confidential, controlled-access, and human-subject data; report findings, uncertainty, and limitations transparently; and follow institutional requirements for research ethics, authorship, data provenance, responsible AI use, and responsible conduct of research.

The University of Pittsburgh is an Equal Opportunity Employer.

The University of Pittsburgh is an equal opportunity employer / disability / veteran.

Assignment Category : Full-time regular

Campus : Pittsburgh

Child Protection Clearances : Not Applicable

Required Attachments : Cover Letter, Curriculum Vitae

The University of Pittsburgh is an equal opportunity employer / disability / veteran.