Senior Computational Scientist - Furman Lab

Senior Computational Scientist – Furman Lab Position Details Salary: $120,000 - $130,000. Start Date: January 15 – February 1, 2026. Location: Buck Institute for Research on Aging (Novato, CA) – Hybrid flexibility available. Appointment: Full-time. Note: This position is contingent upon the Furman Lab being awarded a large funded project in February 2026. About the Furman Lab The Furman Lab integrates systems biology, causal modeling, and advanced AI/ML approaches to understand the biological mechanisms underlying aging, resilience, and physiological decline. Our work integrates large human cohorts, multi-omics data, and digital health measurements to identify actionable molecular drivers of healthspan and develop predictive, translational models. As leaders of Buck Bioinformatics and Data Science Core, we build analytical standards and frameworks that support institute-wide and multi-institutional research collaborations. Position Overview The Senior Computational Scientist will play a central role in a large funded research project focused on identifying causal drivers and mechanistic pathways underlying resilience, aging trajectories, and functional decline. The role includes designing and deploying causal inference pipelines, longitudinal and multiscale temporal models, and multimodal data integration approaches that connect omics, clinical phenotypes, and wearable-derived physiological signals. The position also involves co‑leading the Buck Bioinformatics and Data Science Core and mentoring 2–3 trainees across aging computational biology, systems physiology, and statistical methodology. Key Responsibilities Computational Leadership Lead development of causal inference frameworks (DAG-based modeling, debiased ML, identifiability assessments) to characterize mechanistic drivers of resilience and physiological decline. Build and optimize state-space, Bayesian, and Kalman filter models for longitudinal, irregularly sampled, and multiscale physiological and digital phenotype data. Develop interpretable multimodal models that integrate omics datasets, biomarker panels, wearable data, and clinical outcomes. Address confounding, selection bias, missingness, and temporal heterogeneity using principled statistical and computational approaches, generating translational insights to inform intervention prioritization and hypothesis testing. Core Leadership & Mentorship Co‑lead the Buck Bioinformatics and Data Science Core, helping define analytical standards, workflows, reproducibility practices, and strategic priorities. Mentor 2–3 trainees (postdocs, analysts, graduate students) in computational modeling, systems biology, and statistical methodology. Promote best practices in documentation, reproducibility, and causal reasoning across collaborating teams. Cross‑Functional Collaboration Collaborate closely with experimental scientists, clinicians, AI/ML researchers, and external partners to align modeling approaches with biological and translational objectives. Communicate findings through presentations, manuscripts, data‑sharing deliverables, and reporting associated with the federally funded research program. Qualifications Education PhD in Biostatistics, Statistics, Epidemiology (methods track), Computational Biology, Systems Biology, or a related quantitative field. Technical Expertise Strong experience in causal inference, including DAG construction, confounding structures, selection bias, and identifiability conditions; familiarity with instrumental variables and debiased/orthogonal ML frameworks. Experience with longitudinal and time‑series modeling, including state‑space or Bayesian approaches, irregular sampling, and missing data; experience modeling circadian or physiological rhythms is highly desirable. Experience working with high‑dimensional biological data (e.g., multi‑omics, biomarker discovery) and interpretable biological modeling approaches. Judicious application of machine learning methods, including latent variable models, embeddings, and dimensionality reduction, with demonstrated judgment around when deep learning is appropriate. Proficiency in R as a primary programming language, with experience using packages such as DoubleML, dagitty, grf, KFAS, bssm, lavaan, mgcv, survival, ranger, and torch. Experience with reproducible analytical workflows and version control. Preferred Qualifications Experience with wearables, digital health, or physiological sensor data. Background in survival analysis, health‑outcome modeling, or time‑to‑event frameworks. Experience with single‑cell or pseudotime trajectory analysis. Knowledge of aging biology, geroscience, systems physiology, or resilience science. Publication record in high‑impact biomedical journals. Benefits Comprehensive benefits package (medical, dental, vision, retirement). Visa sponsorship and immigration support, if needed. Access to world‑class analytical infrastructure, Buck core facilities, and multi‑omics platforms. Opportunity to contribute to pioneering research in aging, immunology, and space biosciences. $5,000 relocation support. Equal Employment Opportunity The following questions are entirely optional. To comply with government Equal Employment Opportunity and/or affirming action reporting regulations, we are requesting (but NOT requiring) that you enter this personal data. This information will not be used in connection with any employment decisions, and will be used solely as permitted by state and federal law. Your voluntary cooperation would be appreciated. Learn more. #J-18808-Ljbffr Buck Institute