**The Position** 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. Genentech’s Clinical Pharmacology Modeling & Simulation group seeks a postdoctoral fellow (or artificial intelligence (AI) residency) to advance scientific machine learning for clinical oncology. The project builds a hybrid framework that couples an existing quantitative systems pharmacology (QSP) model of cancer immunotherapy with graph neural networks trained on spatial single-cell tumor microenvironment (TME) data from non-small cell lung cancer (NSCLC). Using high-dimensional datasets, you will learn bi-directional links between local TME interactions and systemic immunity, create digital twins to test treatment hypotheses, and inform patient-selection strategies. You will integrate spatial transcriptomics with clinico-genomic/response data, design training over baseline, on-treatment and post-treatment time points, and model treatment effects with both mechanistic and data-driven components. You will work with Clinical Pharmacology, Research Oncology, Biomarker Development and AI/ML peers, publish work not tied to a specific program, and help disseminate findings internally and externally. This is a high-impact role with strong mentorship and opportunities for first-author publications. The appointment is from 2 to 4 years. **The Opportunity:** In addition to developing a hybrid mechanistic–ML framework for the comprehensive assessment of immunotherapy and combination strategies, the fellow will compile, harmonize, and quality-check all required data (e.g., spatial/single-cell, genomics, clinical outcomes); set up and maintain the end-to-end computational stack (data processing, model training/inference, simulation, visualization) using reproducible practices (Git version control, automated tests/CI); work closely with modeling and AI/ML colleagues while collaborating across Oncology, Biomarker, and Clinical teams; and deepen expertise in tumor immunology and clinical aspects of anti-cancer treatments as needed. Other responsibilities include: + Present results in accessible terms, and actionable recommendations, at cross-functional teams, department meetings, and review committees. + Prepare posters and talks for internal seminars and external conferences. + Write manuscripts (lead and co-author) summarizing methods, results, and insights. + Adapt and thrive in a collaborative, interactive, and team-oriented environment. **Who You Are:** + Ph.D. in physics, applied mathematics, statistics, computer or computational sciences, or in an engineering field such as biomedical informatics, or a related discipline. + Required experience in developing deep-learning systems; graph neural networks & representation learning (VAEs/transformers) preferred. + Experience in statistical modeling & ML for high-dimensional data (training, evaluation, uncertainty) is required. + A publication record of substantial/influential work is expected. **Preferred Qualifications:** + Experience with ODE-based systems modeling (especially of biological systems), such as Quantitative Systems Pharmacology (QSP), would be highly preferable. Knowledge of neural-ODE or Universal Differential Equations is a plus + Proficiency in Python or Julia (Python preferred) & modern ML tools (PyTorch, JAX or TensorFlow); hands-on knowledge of Matlab is a plus; modern software practices (Git version control, automated tests/CI). + Experience with single-cell and/or spatial transcriptomics & integration with clinical/response data, or strong motivation to learn. + Familiarity with causal inference or generative modeling for virtual populations/digital twins advantageous. + Strong communication and interpersonal skills, enthusiasm to contribute to a multidisciplinary environment, and a drive to solve challenging scientific problems. + We are looking for creative, resourceful, and intellectually curious individuals who are eager to learn and discover. **Postdoc Program** Elevate your research career to new heights with Genetech’s Postdoctoral Program! Join a prestigious community of early career scientists and kick-start your journey toward becoming a scientific leader in biotechnology and bioengineering. With competitive salaries and fully funded research expenses, you can dedicate yourself to groundbreaking research that aligns with Genentech strategic ambitions. The postdoc program is designed to empower recent Ph.D. graduates to conduct world-class research, publish in top-tier journals, and build a robust scientific network. By joining us, you will receive the mentorship and support necessary to develop into an independent scientist and leader. The Genentech Postdoc Program (http://careers.gene.com/us/en/students-postdocs) provides access to world-class seminars, professional development workshops, and networking opportunities. Apply now to the Genentech Postdoctoral Program and unlock your full potential! The expected salary range for this position based on the primary location of South San Francisco California is $110,000 to $120,000. Actual pay will be determined based on experience, qualifications, geographic location, and other job-related factors permitted by law. This position also qualifies for the benefits detailed at the link provided below. Benefits (https://roche.ehr.com/default.ashx?CLASSNAME=splash) Relocation benefits are available for this job posting. \#LI-PL1 \#postdoc Genentech is an equal opportunity employer. It is our policy and practice to employ, promote, and otherwise treat any and all employees and applicants on the basis of merit, qualifications, and competence. The company's policy prohibits unlawful discrimination, including but not limited to, discrimination on the basis of Protected Veteran status, individuals with disabilities status, and consistent with all federal, state, or local laws. If you have a disability and need an accommodation in relation to the online application process, please contact us by completing this form Accommodations for Applicants (https://docs.google.com/forms/d/e/1FAIpQLSdZWlsbfQOvFVIQgHE\_iDzWUTlhZvj6FytIzjS7xq6IGh1H5g/viewform) .