Post Doctoral Fellow - Biostatistics and Data Science

University of Maryland, Baltimore4 months ago
Baltimore, MD, United States
On-site
Full-time
Junior Level (1-3 years)

Job Description

POSTDOCTORAL FELLOW

The Maryland Psychiatric Research Center (MPRC) at the University of Maryland School of Medicine is recruiting a Postdoctoral Fellow for the Biostatistics and Data Science Laboratory led by Dr. Shuo Chen.

The Maryland Psychiatric Research Center (MPRC) is an internationally renowned research center, which is dedicated to providing treatment to patients with schizophrenia and related disorders, educating professionals and consumers about schizophrenia, and conducting basic and translational research into the manifestations, causes, and treatment of schizophrenia.

Essential Functions:

  • Developing new statistical methods to model multimodal neuroimaging data, multi-omics data, electronic health record (EHR) and genetics data which are measured by longitudinally and cross-sectionally.
  • Developing and applying machine learning and AI approaches to identify interactive topological relationships between various imaging modalities and multi-omics during aging and development.
  • Implementing computationally intensive algorithms on high-performance computational clusters.
  • Developing AI/ML-based causal inference models for real-world evidence applications (e.g., target trial emulations- TTE)
  • Developing data integration methods to combine multiple massive datasets (UKBB, HCP, ENIGMA, and etc) and local trials and observational studies (e.g, UMMS EHR).
  • Manuscript drafting and preparation.
  • Performing other duties as assigned.

Qualifications

Minimum Qualifications:

Education: A Ph.D. degree in a quantitative field such as statistics, biostatistics, bioinformatics, computational biology, human genetics, computer science, or a closely related area is required.

Experience: Demonstrated experience in strong optimization computational skills, proficiency in one of the programming languages R, Matlab, and Python, and expertise in high-performance clusters are required.

Knowledge, Skills, and Abilities:

  • Knowledge in advanced statistical theory and methods.
  • Strong optimization and computational skills to process large-scale medical images and analyze multi-modal data effectively.
  • Excellent interpersonal skills, including a positive and professional orientation towards faculty, staff, university officials, partner organizations, and other stakeholders.

Compensation and Benefits

The stipend for Postdoctoral Fellows is competitive and aligned with the number of full years of relevant postdoctoral experience. Relevant experience may include research, teaching, clinical duties, internships, or other work in a health-related field beyond the qualifying doctoral degree. Stipend levels are determined to meet or exceed the current NIH Stipend Levels listed on the website. Postdoctoral Fellows at the University of Maryland also receive a comprehensive benefits package that includes 10 days of annual leave, 15 days of sick leave, 3 personal days, all university-recognized holidays, and may also qualify for additional leave options.

Application Process

For immediate consideration, please send a cover letter and a recent CV, including names and contact information of three references, to this link

To apply for this position or to seek additional information, please contact Dr. Chen at ShuoChen@som.umaryland.edu

For additional questions after application, please email at AAmante@som.umaryland.edu

Equal Opportunity Statement

UMB is an equal opportunity/affirmative action employer. All qualified applicants will receive consideration for employment without regard to sex, gender identity, sexual orientation, race, color, religion, national origin, disability, protected Veteran status, age, or any other characteristic protected by law or policy.

Job Information

Job: Postdoctoral Fellows

Organization: School of Medicine - Clinical Departments

Job Posting: Feb 6, 2026

Required Skills

Python
High-Performance Computing
Data Integration
Medical Imaging
Causal Inference
Biostatistics
EHR Analysis
Matlab
R
Data Science
Statistical Methods
Computational Biology
AI
Machine Learning
Optimization