1、Lead the development and application of AI/ML models for de novo design and optimization of linear and cyclic peptides. 2、Focus on generating peptides with enhanced druggability, including improved binding affinity, stability, and desired pharmacokinetic properties. 3、Implement and adapt modern deep learning architectures for sequence and structure-based peptide generation. 4、Design and execute computational workflows to predict and optimize peptide folding, conformational dynamics, and target interaction. 5、Collaborate with experimental teams to validate computational predictions and rapidly iterate on design cycles. 1、Education: Ph.D. in Computational Biology, Bioinformatics, Computer Science, or a related field, OR 3-5 years of relevant industry experience. 2、Peptide Expertise: Demonstrated experience in the computational design, modeling, or analysis of peptides and cyclic peptides. 3、Machine Learning: Expert-level proficiency in PyTorch or a similar deep learning framework. 4、Generative Models: Strong theoretical and practical knowledge of modern generative models, including Transformer, Diffusion, and Reinforcement Learning (RL) training frameworks, specifically applied to biomolecular generation. 5、Computational Modeling: Experience with tools and methods for peptide conformational sampling, docking, and property prediction.