Senior Computational Scientist – Furman Lab

Senior Computational Scientist – Furman Lab

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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.

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.

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. This individual will design and deploy causal inference pipelines, longitudinal and multiscale temporal models, and multimodal data integration approaches connecting omics, clinical phenotypes, and wearable‑derived physiological signals. The role also includes co‑leading the Buck Bioinformatics and Data Science Core and mentoring 2–3 trainees across aging computational biology, systems physiology, and statistical methodology.

• 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.

• PhD in Biostatistics, Statistics, Epidemiology (methods track), Computational Biology, Systems Biology, or a related quantitative field.

• 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…