Location: Rochester, Minnesota
Position Type: Full-time, on-site, fixed-term (2-3 years)
Start Date: November 2026
Overview
We are seeking a highly motivated and innovative postdoctoral research fellow to join the multidisciplinary research team of Dr. Akhilesh Pandey at Mayo Clinic, Rochester, working at the intersection of digital pathology, artificial intelligence (AI), and machine learning (ML).
This position offers an exciting opportunity to develop and apply cutting-edge computational approaches to large-scale biomedical datasets, with a focus on advancing precision medicine in cancer. The successful candidate will work closely with computational scientists, pathologists, and clinicians to build robust AI-driven solutions for biomarker discovery, disease characterization, and translational research.
Key Responsibilities:
- Develop, implement, and optimize machine learning/deep learning models for digital pathology image analysis
- Analyze large-scale histopathology, omics, and clinical datasets
- Design pipelines for image preprocessing, segmentation, feature extraction, and predictive modeling
- Collaborate with cross-functional teams to integrate imaging data with genomic/transcriptomic/proteomic data
- Contribute to study design, data interpretation, and dissemination of findings in high-impact peer-reviewed journals and present at conferences
Required Qualifications:
- Ph.D. in Bioinformatics, Computational Biology, Computer Science, Biomedical Engineering, or a related quantitative discipline
- Strong background in machine learning/deep learning (e.g., CNNs, transformers, vision models)
- Experience working with foundation models (e.g., vision-language models, large multimodal models, or pre-trained foundation models for biomedical imaging)
- Proficiency in Python and relevant libraries (e.g., PyTorch, TensorFlow, scikit-learn)
- Experience with image analysis pipelines and handling large-scale datasets
- Proven ability to conduct independent research and publish results
- Strong problem-solving skills and excellent communication abilities
Preferred Qualifications:
- Experience in digital pathology or computational pathology
- Hands-on experience in fine-tuning, adapting, or deploying foundation models for biomedical or imaging applications
- Familiarity with multimodal data integration (e.g., imaging + spatial proteomics, genomics, transcriptomics)
- Knowledge of self-supervised learning, contrastive learning, or representation learning approaches
- Experience with high-performance computing, cloud platforms, or distributed training
- Prior experience working in a collaborative biomedical research environment
What We Offer
- Access to state-of-the-art computational resources and high-quality datasets
- Opportunities to collaborate with leading experts in pathology, multi-omics technologies, and AI
- A highly interdisciplinary and supportive research environment
- Competitive salary and benefits package
- Strong support for career development, networking, and academic advancement
Application Instructions:
Interested candidates should submit:
- Cover letter outlining research interests and experience
- Curriculum Vitae (CV)
- Contact information for 2–3 references
Applications will be reviewed on a rolling basis until the position is filled.
