Postdoctoral Fellow – Computational Pathology and Multimodal AI for Prostate Cancer

Location: Robert Ho Building: 2635 Laurel Street, Vancouver, BC, V5Z 1M9
Department: Department of Urologic Sciences, University of British Columbia
Appointment Level: Commensurate with qualifications and experience

 

Position Summary

The Mannas Lab at the Vancouver Prostate Centre is seeking a highly motivated computational scientist or translational researcher to help build the lab’s computational modeling and data integration program in prostate cancer. This role is intended for a candidate who can work at the interface of imaging, molecular data, clinical data, and machine learning. The successful candidate will help develop and validate models that integrate diverse data types, including microscopy or pathology images, diagnostic imaging, molecular based data (e.g. sequencing, metabolism), and clinical variables, to better understand aggressive prostate cancer and improve translational research workflows.

This is an opportunity to help build the computational backbone of a growing translational lab, including model development, multimodal data integration, reproducible analytic pipelines, and biologically grounded interpretation.

Depending on qualifications and experience, the successful candidate may be appointed as a Senior Research Assistant/Technician, Research Associate, or Postdoctoral Fellow.

Core Responsibilities

The successful candidate will contribute to the development of the lab’s computational research program, including:

• Building, training, evaluating, and refining machine learning or deep learning models using imaging, molecular, and clinical datasets
• Developing multimodal approaches that integrate pathology or microscopy images with RNA-seq, MRI/PET, DNA-seq, spatial transcriptomic, proteomic, metabolomic, or other molecular data
• Designing robust data workflows for curation, harmonization, labeling, preprocessing, and quality control
• Supporting image annotation and label integration with investigators, pathologists, and collaborators
• Implementing rigorous validation frameworks, including patient-level splitting, external validation, calibration assessment, and benchmarking
• Developing interpretable models and analyses that connect imaging phenotypes to biologic or clinical outcomes
• Establishing reproducible code, documentation, and data practices for collaborative research
• Generating analyses, figures, and summaries for manuscripts, grants, abstracts, and presentations
• Depending on appointment level, contributing to study design, mentorship, and scientific writing

Required Qualifications

• MSc or PhD in bioinformatics, computer science, computational biology, biomedical engineering, medical biophysics, pathology, imaging science, or a related field
• Strong programming skills in Python
• Experience building or evaluating machine learning or deep learning models for biomedical imaging, pathology, microscopy, radiology, omics, or multimodal biomedical data
• Experience working with high-dimensional molecular datasets such as transcriptomic, genomic, spatial, or other sequencing-derived data
• Experience integrating multiple data types into coherent analytic workflows
• Strong understanding of model evaluation, validation, and reproducibility
• Excellent organizational, quantitative, and problem-solving skills
• Ability to work effectively in a multidisciplinary translational research environment

Preferred Qualifications

• Experience in computational pathology, digital pathology, or image-based biomarker discovery
• Experience with frameworks such as PyTorch, TensorFlow, MONAI, scikit-learn, OpenSlide, QuPath, or similar
• Experience linking image features with molecular labels, including RNA-seq, DNA-seq, spatial transcriptomics, or related datasets
• Experience with multimodal modeling involving clinical variables and imaging or molecular data
• Familiarity with optical imaging methods such as stimulated Raman histology (SRH), Raman imaging, CARS, multiphoton, confocal, or related microscopy platforms
• Familiarity with prostate cancer, oncology, biomarker development, or translational cancer research
• For postdoctoral-level applicants: evidence of first-author publications and the ability to lead independent methodological work

Ideal Candidate

The ideal candidate will:

• Be excited by building the computational and modeling arm of a translational lab
• Understand how to connect imaging, biology, and clinical context rather than treating modeling as a purely technical exercise
• Be comfortable working with imperfect real-world datasets and improving them into usable research assets
• Have the maturity to suggest computational direction, not just execute assigned analyses
• Be motivated by translational work with clear clinical relevance

Appointment Levels

The final title will depend on training, experience, and level of independence:

Senior Research Assistant/Technician
Strong technical candidate with experience implementing pipelines, curating datasets, supporting model development, and maintaining reproducible workflows.

Research Associate
Experienced candidate able to independently drive computational projects, multimodal model development, and collaborative analysis.

Postdoctoral Fellow
Independent scientist able to lead methods development, publish first-author work, help shape scientific direction, and contribute to grant development.

Application Materials

Please submit your application to Dr. Mile Mannas, at miles.mannas@ubc.ca

• Cover letter
• Curriculum vitae
• Contact information for 2–3 references
• Optional: GitHub, code portfolio, preprints, or representative projects

Applications will be reviewed on a rolling basis until the position is filled.

How to apply?

Please send applications to miles.mannas@ubc.ca and refer to reference number PDFO-59205.

Desired start date: 01 Aug 2026
Duration: Fixed term / Temporary
Contract Type: Full Time

Equity and diversity are essential to academic excellence. An open and diverse community fosters the inclusion of voices that have been underrepresented or discouraged. We encourage applications from members of groups that have been marginalized on any grounds enumerated under the B.C. Human Rights Code, including sex, sexual orientation, gender identity or expression, racialization, disability, political belief, religion, marital or family status, age, and/or status as a First Nation, Metis, Inuit, or Indigenous person.