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.

Location: Eastern England, GB

Posted Date: 6/15/2025