Develop novel cell embeddings that integrate multi-omics foundation models— transcriptomics, proteomics, epigenomics, and metabolomics—to capture comprehensive cellular signatures. Your work will enable precise predictions of drug effects, driving innovation in drug discovery.
Key Responsibilities:
•Model Development:Design deep learning models integrating diverse omics data to create robust cell embeddings for digital twin technology.
•Multi-Omics Integration:Develop and refine foundation models across omics platforms into a unified cell representation.
•Collaboration:Work with experts in bioinformatics, drug discovery, and AI to validate models and integrate multi-modal data.
•Client & Partner Engagement:Support product and service teams in translating AI models into real-world drug discovery applications.
•Research Leadership:Stay at the forefront of AI and omics advancements, contributing to scientific publications and innovation.
Preferred Qualifications:
1.PhD/Postdoc in Computer Science (or related fields): Publications in top ML conferences (e.g., NeurIPS, ICLR, ICML, CVPR).
2.Strong ML/Applied Math Background:Expertise in advanced ML techniques.
3.Deep Learning Experience:Building and scaling AI models for omics or high dimensional biological data.
4.Multi-Omics Integration: Experience developing foundation models across omics datasets.
5.Collaborative Mindset:Track record of success in interdisciplinary teams and cross-functional projects.
