Senior Computational Scientist – Furman Lab

Novato, CA - USA

Yearly Salary: USD 120000 - 130000

Posted on: 30+ days ago

Vacancies: 1 Vacancy

Job Summary

POSITION DETAILS
Salary: $120000 - $130000
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. 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 23 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 23 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 usingpackages 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.
$5000 relocation support

TO APPLY
Interested candidates should click the Apply button to complete the online application. Please upload both your CV and a document that includes a brief statement of your interests plus the names/contact information of 3 references.

Required Experience:

Senior Manager

POSITION DETAILSSalary: $120000 - $130000Start Date: January 15 February 1 2026Location: Buck Institute for Research on Aging (Novato CA) Hybrid flexibility availableAppointment: Full-timeNote: This position is contingent upon the Furman Lab being awarded a large funded project in February 2026.AB...

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.