CMPTL & DATA SCI RSH SPEC 4 RP

Involves a hybrid of multiple computational / data / Cyberinfrastrcture (CI) dominated fields such as, but not limited to, bioinformatics, geological information services (GIS), data analytics, and computational chemistry. Applies computational, computer science, data science and cyber infrastructure (CI) research and development principles, with relevant domain science knowledge, to perform research and technology integration and development. Responsibilities include research, design, development, analysis, operation, and support of high performance computing (HPC) and data science research, software, tools and hardware resources. Develops data algorithms and performs computations, statistical analyses, interpretation and reporting of research. This specialty / function exists for those positions whose primary responsibility is to do research and use computational and data science technology as a tool to accomplish the research.

The Preclinical Design and Clinical Translation of Regimens for Tuberculosis (PReDiCTR-TB) Consortium is a 21st-century, model-informed drug development (MIDD) platform integrating computational science, translational pharmacology, and global clinical insight to accelerate the design and delivery of transformative TB regimens.

Positioned at the intersection of data, biology, engineering, econometrics, and decision science, PReDiCTR-TB functions as a strategic intelligence engine for regimen development, linking preclinical evidence, synthetic experiments, mechanistic models, and clinical data into a unified predictive framework. By embedding quantitative systems pharmacology (QSP), AI-driven analytics, and probabilistic decision modeling throughout development, the consortium enables real-time prioritization of regimens with the highest probability of clinical success.

Rather than advancing compounds in isolation, PReDiCTR-TB takes a regimen-first, translation-driven approach that optimizes combinations, dosing strategies, and treatment durations through iterative simulation-validation cycles grounded in human-relevant biology. This framework helps reduce reliance on costly empirical experimentation while increasing translational fidelity from bench to bedside.

The consortium delivers:

• Predictive insights that de-risk development earlier
• Optimized trial designs informed by mechanistic and statistical rigor
• Faster and more confident go/no-go decisions across the R&D lifecycle

PReDiCTR-TB is redefining how infectious disease regimens are designed, prioritized, and translated into clinical impact.

Custom Scope

Model-informed drug development (MIDD) and AI in Clinical Trials market is experiencing a massive shift, projected to skyrocket from $1.35 billion to $2.74 billion by 2030. Data and model-driven efficiencies in patient selection, drug repurposing, and real-time trial monitoring are fundamentally compressing drug development timelines.

At the UCSF Savic Lab, we aren't just reacting to this shift—we are driving it. We are moving past traditional, isolated data storage models to establish a modern, highly interconnected, and secure data & model infrastructure.

We are seeking a solution-minded, highly technical, and mission-driven Distributed Data Pipeline Architect. In this role, you will design, build, lead implementation, and operate a research-grade, scale-distributed data architecture. This framework will serve as the foundation for advanced analytics, multi-institution translational science, and 21st-century accelerated drug development decision support.

Department Overview

The Savic Lab is a global leader in model-informed drug development for infectious diseases and serves as a quantitative innovation hub for translational pharmacology, AI-enabled modeling, and next-generation regimen design. The laboratory conducts groundbreaking research across TB, HIV, malaria, pediatric infectious diseases, translational PK/PD, and systems pharmacology. Through its leadership role in PReDiCTR-TB, the lab collaborates with international partners to integrate computational science, mechanistic modeling, and clinical translation into actionable strategies that improve global health outcomes.

UC San Francisco seeks candidates whose experience, teaching, research, or community service has prepared them to contribute to our commitment to diversity and excellence. The University of California is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion.