Postdoctoral Fellow- Regulatory Language Understanding (ReLU) Lab.

A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Genentech.

A postdoctoral fellow position is available in the ReLU lab, led by Gokcen Eraslan within the Biology Research AI Development (BRAID) department of Genentech's Computational Sciences Center of Excellence (CS-CoE) organization. This role offers a unique opportunity to contribute to cutting-edge research at the intersection of artificial intelligence and regulatory genomics, developing and applying state-of-the-art computational methods to critical scientific challenges.

The primary focus of this postdoctoral role will be developing and applying advanced AI models and sophisticated multi-agent systems to address pressing challenges in regulatory genomics. 

Key research areas include:

Mechanisms of Common and Rare Genetic Variations: Investigate the molecular mechanisms underlying genetic variations, elucidating how these variants influence gene regulation and contribute to health and disease states

Design of Non-Coding Genome Edits: Develop innovative machine learning approaches for designing precise non-coding genome edits, focusing on how non-coding alterations influence gene regulation and cellular function

Disruptions in Post-Transcriptional Regulatory Processes: Analyze how alterations in RNA processing, stability, and translation contribute to cellular dysfunction and disease pathogenesis using mechanistic or quasi-mechanistic models

Multi-Agent AI Systems for Scientific Discovery: Pioneer the development of multi-agent computational systems where specialized AI agents collaborate to solve complex genomic challenges, including data analysis, hypothesis generation, and experimental design optimization.

We seek a highly motivated postdoctoral candidate with expertise in AI, machine learning, and computational biology. The successful applicant will leverage these skills to address complex biological questions. Essential qualifications include a strong publication record, excellent communication abilities, good sense of humor and a collaborative mindset to thrive in our dynamic research environment.

The Opportunity:

• Work closely with experimental colleagues to build, train, and evaluate cutting-edge AI models and multi-agent systems for regulatory genomics
• Leverage multimodal high-dimensional data to investigate relationships between genetic variations and gene regulation in disease contexts
• Conduct exploratory research in a fast-paced environment with potential for significant impact on understanding and treating diseases related to gene dysregulation
• Receive technical and scientific mentorship from computational and experimental colleagues while developing and testing biological hypotheses
• Present at international scientific conferences and publish models and insights in high-impact journals

Who You Are:

• Ph.D. in Computational Biology, Bioinformatics, Computer Science, Machine Learning, or related field with focus on genomics or regulatory biology
• Demonstrated proficiency in Python and machine learning frameworks (e.g. PyTorch, Jax, scikit-learn) applied to genomic datasets
• Experience with various sequence modeling architectures and interpretable AI methods (attribution methods including SHAP, Integrated Gradients, etc.)
• Proven ability to communicate complex bioinformatics concepts to both technical and non-technical audiences, evidenced by first-author publications in peer-reviewed journals
• Independent, highly motivated, and collaborative researcher able to work effectively with multidisciplinary teams of computational and experimental biologists

Preferred Qualifications:

• Experience with genomic foundation models (e.g., Decima, Enformer, Evo2)
• Experience with RNA sequence models (e.g. Saluki, Orthrus, RiboNN)
• Background in bulk and/or single-cell omics data analysis (genomics, transcriptomics, scRNA-seq, Perturb-seq) with deep understanding of gene regulation
• Familiarity with agentic frameworks (LangChain, LangGraph, AutoGen, Google ADK) and their applications in biological contexts
• Experience applying computational linguistics, NLP, and NLU practices to regulatory genomic language, including sequence grammar analysis, motif semantics, and regulatory syntax modeling
• Passion for translational research in genomics, drug discovery, and therapeutic development
• Strong ideation skills with ability to rapidly prototype and evaluate new concepts
• Intellectual curiosity and courage to boldly pursue innovative research directions
• Ability to excel in a fast-paced, evolving research environment