This position focuses on the development and application of advanced statistical, deep learning, and computational methods for spatial transcriptomics and digital pathology in cancer research. The trainee will gain hands-on experience in statistical genomics, deep learning model development, software implementation, and effective communication of research findings in a multidisciplinary environment.

LEARNING OBJECTIVES
1. To acquire a deep understanding of state-of-the-art statistical genetics and genomics methods, and recent advances in deep learning and computational methods for spatial transcriptomics through critical literature review.
2. To learn how to develop deep learning models and apply advanced computational tools for spatial transcriptomics data and digital pathology data in cancer research.
3. To learn the process of scientific publication and effectively communicate research in a multi-disciplinary environment.

ELIGIBILITY REQUIREMENTS
Applicants should have a strong background in computational biology, bioinformatics, statistics, or a related field. Prior experience with deep learning frameworks, spatial transcriptomics, or digital pathology is desirable. Strong programming skills in Python or R and experience with statistical genomics methods are preferred.

ADDITIONAL APPLICATION INFORMATION
Candidates need to demonstrate a strong commitment to research, self-motivation, and the ability to work in an interdisciplinary research environment. The selected candidate will actively engage in weekly mentoring meetings, participate in scientific discussions, and contribute to publications and presentations.

POSITION INFORMATION
This position (full-time or part-time) provides a stipend between $28,000 and $36,000.02/14/2025 Apply