Deep Learning Research Scientist

Acting on behalf of its client - a French start-up in the biotech field - Tomorrow Jobs is currently looking for a Deep Learning Research Scientist - Foundation Model on Omics data– Full remote or Paris

Overview:

In this role, you will develop a novel cell embedding that integrates multiple omics foundation models—such as transcriptomics, proteomics, epigenomics, and metabolomics—to capture comprehensive, multi-dimensional cellular signatures. Your innovations will be pivotal in predicting drug effects on cell types and tissues, transforming raw data into actionable insights in drug discovery.

Key Responsibilities:

•Deep Learning Model Development : Design and implement large-scale deep learning models that integrate diverse omics datasets to build robust cell embeddings. These embeddings will serve as the foundation for our digital twin technology, enabling precise predictions of drug effects at both cellular and tissue levels.

•Multi-Omics Integration : Develop and refine foundation models across various omics platforms, combining them into a unified cell embedding that reflects the complex molecular landscape of cells.

•Digital Twin development : Design and implement large-scale causal model to predict cell response to perturbations.

•Cross-Disciplinary Collaboration : Partner with experts in omics, bioinformatics, and drug discovery to ensure seamless integration of multi-modal data and validate model predictions through collaborative research efforts.

•Client & Partner Engagement : Work closely with the product and service teams on projects with clients and strategic partners, translating advanced AI models into impactful drug discovery solutions.

•Research Leadership : Continuously monitor emerging trends in AI and omics technologies, contributing to scientific publications and driving innovation within our international, interdisciplinary team.

What We’re Looking For :

• A visionary researcher passionate about leveraging deep learning to solve complex biological challenges in drug discovery.

• Proven expertise in integrating multi-omics data to develop innovative computational models that generate actionable insights.

Qualifications :

•PhD or Postdoctoral Experience in Computer Science : Demonstrated expertise, evidenced by publications in top-tier machine learning conferences (e.g., NIPS, ICLR, ICML, UAI, CVPR).

•Strong Foundation in Machine Learning/Applied Mathematics : Robust academic background in advanced ML techniques and applied mathematics.

•Experience with Large-Scale Deep Learning Models : Proven track record in building and scaling AI models, particularly those applied to omics or other high-dimensional biological data.

Experience with Probabilistic Graphical Models and Causal Inference : Proven track record in designing, building, and scaling advanced causal inference frameworks, including Bayesian networks, structural equation models, and counterfactual analysis methods.

•Multi-Omics Integration Expertise : Experience in developing and combining foundation models across different omics datasets to create unified cell embeddings.

•Collaborative Mindset : Prior success working within interdisciplinary teams and managing cross-functional projects.