Track V Bio-Intelligent Manufacturing (Proteins and Synthetic Biology): Subtrack 2. Chassis Cells(J28025)

Foundation Models

1.Develop a microbial genomic foundation model capable of learning natural evolutionary principles and understanding sequence–function relationships, enabling precise prediction and efficient design of strain characteristics to enhance target industrial traits (e.g., thermotolerance, rapid growth, shear resistance).

2.Develop a biological hydrogen production foundation model capable of learning hydrogen-producing metabolic pathways and microbial community succession dynamics, enabling precise prediction and rapid design of hydrogen production efficiency, oxygen tolerance, and substrate utilization capacity.

Vertical Applications

• <1.Design> of industrial microbial chassis strains
• 2.Biological hydrogen production

Affiliated Branches

• Changzhou Branch (Changzhou)
• Hangzhou Branch (Hangzhou)
• Hainan Branch (Hainan)

(1) Preferred Background

AI, Computer Science, Bioinformatics, Computational Biology, Molecular Biology, or related disciplines. Strong interest in AI-driven biological modeling and biomanufacturing

(2) Preferred Skills

• Experience with foundation models, or generative models
• Proficiency in Python and experience with scientific computing libraries (e.g., NumPy, PyTorch, TensorFlow)
• Basic understanding of machine learning or deep learning methods
• Experience with large-scale biological data analysis (genomics, proteomics, or other omics datasets) is preferred

(3) Personal Qualities

• Strong analytical thinking and problem-solving ability
• Interest in interdisciplinary research combining AI and biology
• Self-motivated with strong learning ability
• Good teamwork and communication skills