Track III Spatiotemporal Omics (Driven by Multi-Tissue Biobanks): Subtrack 7. Oncology(J28017)

Foundation Model

Supports reinforcement training of the Spatial Pathology foundation model to build a state-of-the-art model for malignant tumors.

Vertical Applications

• 1.Pathological diagnosis, grading, and treatment guidance
• 2.Tumor screening and monitoring
• 3.Treatment response and prognosis prediction

Affiliated Branch

• Chongqing Branch (Chongqing)
• Hangzhou Branch (Hangzhou)
• Beijing Branch (Beijing)

(1) Preferred Background

Cancer Biology, Bioinformatics, Computational Biology, AI, Systems Medicine, Computer Sciences, Mathematics.

(2) Core Technical Skills

Programming skills(R/Python);Tumor multi-omics data and clinical data analysis and interpretation; deep learning models.

(3) Modeling Competency

Ability to integrate multi-omics data to empower tumor models; understanding of foundation model and fine-tuning.

(4) Preferred Qualifications

Experience or have published work related to cancer genomics, pathology AI, or drug response modeling, or familiar with large foundation models. Ability to communicate with clinical expert.

(5) Personal Traits

Resilient under research complexity, clinically aware, and driven by precision oncology impact, stay hungry and curiosity.