Lead Computational Biologist & Deep Learning Engineer

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.