Applies advanced computational, computer science, data science, statistical, and quantitative modeling principles, together with domain expertise in pharmacology, drug development, and translational science, to perform research and technology development supporting model-informed drug development (MIDD). Responsibilities include the design, development, implementation, validation, and application of computational models, machine learning approaches, simulation frameworks, and quantitative decision-support tools used to advance drug regimen development and clinical translation.
The position integrates diverse preclinical, clinical, and real-world datasets to develop predictive models that support regimen optimization, dose selection, trial design, and translational decision-making. Research activities may include pharmacometric modeling, quantitative systems pharmacology (QSP), mechanistic and Bayesian modeling, artificial intelligence and machine learning methods, statistical analyses, and development of computational workflows and scientific software.
This specialty exists for positions whose primary responsibility is to conduct independent quantitative research and use computational and data science technologies to advance biomedical and translational research.
The Savic Integrated Pharmacology Laboratory at UCSF is seeking a Ph. D. level Quantitative Scientist, Pharmacometrician, Computational Scientist, Data Scientist, or Translational Modeler to play a scientific leadership role within the PReDiCTR-TB Consortium, a global collaboration accelerating next-generation tuberculosis (TB) treatment regimens. This role sits at the forefront of model-informed drug development (MIDD), AI-enabled translational science, and quantitative decision-making for infectious diseases.
The successful candidate will help shape quantitative strategies that directly influence TB regimen design, dose optimization, translational prediction, and development decisions across academia, industry, and regulatory stakeholders. We are particularly interested in intellectually curious, self-directed scientists who thrive at the intersection of computational science, biology, engineering, pharmacology, econometrics, and real-world decision-making.
This is not a traditional support role. This is an opportunity to help define how AI, quantitative modeling, and translational science reshape infectious disease drug development globally. What You’ll Work On You will contribute to high-impact translational and computational research programs that may include: Model-informed drug development (MIDD) strategies for TB regimen optimization AI-driven drug and regimen design Quantitative systems pharmacology (QSP) Translational PK/PD and mechanistic modeling Bayesian and probabilistic decision frameworks Clinical trial simulation and optimal design Toxicokinetics and translational safety modeling Pharmacogenomics and precision medicine approaches Multi-scale integration of preclinical, clinical, and real-world datasets Synthetic experiments and simulation-driven regimen prioritization Scalable computational pipelines and scientific software development Key Responsibilities Lead or contribute to quantitative modeling and simulation strategies for TB drug regimen development Build and implement computational frameworks that support translational and clinical decision-making Integrate multi-source datasets including preclinical, animal, clinical, and real-world data Develop predictive models that improve regimen selection, dose optimization, and translational fidelity Influence modeling strategy across a multi-institutional international consortium Communicate complex quantitative insights to scientific, clinical, operational, and strategic stakeholders Contribute to publications, consortium deliverables, and scientific presentations Collaborate across academia, industry, and regulatory environments Mentor junior scientists and help foster an interdisciplinary quantitative research culture Who We’re Looking For We are seeking scientists who: Think independently and challenge assumptions constructively Enjoy solving difficult translational and quantitative problems Are comfortable operating across disciplines Can move between theory, computation, biology, and decision-making Want to build impactful models rather than simply analyze datasets Are excited by the opportunity to influence real-world global health outcomes We strongly encourage applicants from adjacent quantitative disciplines who are interested in expanding into pharmacometrics and translational modeling.
Preferred Scientific Backgrounds Candidates may come from one or more of the following fields: Pharmacometrics Computational Biology Systems Pharmacology Pharmacogenomics Econometrics Biostatistics Machine Learning / AI Scientific Computing Bioinformatics Toxicokinetics Applied Mathematics Physics Engineering Computer Science Decision Science Quantitative Pharmacology Important Note About Qualifications We are not looking for candidates who possess every possible technical skill listed in this description.
PReDiCTR-TB is intentionally designed as an interdisciplinary consortium where impactful innovation emerges from teams with complementary expertise. We highly value candidates with deep strength in one or several relevant domains who are excited to collaborate across disciplines and expand their quantitative toolkit. Candidates with strong expertise in the following areas are particularly encouraged to apply, even if they do not have formal training across all areas of pharmacometrics: Pharmacogenomics Econometrics AI/ML-driven drug design Scientific Python programming Toxicokinetics QSP Bayesian modeling Translational PK/PD Computational infrastructure Department Overview: The Savic Integrated Pharmacology Laboratory in the Department of Bioengineering and Therapeutic Sciences at the University of California, San Francisco (UCSF) is a global leader in model-informed drug development (MIDD) for infectious diseases.
The laboratory develops and applies quantitative approaches, including pharmacometrics, quantitative systems pharmacology (QSP), machine learning, translational pharmacology, and mechanistic modeling, to accelerate the development of optimized treatment regimens for tuberculosis (TB), HIV, malaria, and other diseases affecting global health. The laboratory leads and coordinates the Preclinical Design and Clinical Translation of Regimens for Tuberculosis (PReDiCTR-TB) Consortium , an international collaboration that integrates computational science, translational pharmacology, clinical data, and quantitative decision science to improve the efficiency and success of TB drug development.
Through the use of predictive modeling, simulation, artificial intelligence, and advanced analytics, the consortium supports regimen selection, dose optimization, trial design, and translational decision-making across the drug development lifecycle. The Savic Lab maintains a highly collaborative and interdisciplinary research environment that brings together pharmacometricians, computational scientists, data scientists, engineers, clinicians, and biologists to address complex challenges in infectious disease drug development.
The laboratory collaborates extensively with academic institutions, government agencies, nonprofit organizations, and pharmaceutical and biotechnology partners worldwide to translate scientific discoveries into improved patient outcomes. DUTIES & ESSENTIAL JOB FUNCTIONS Identify the functions or tasks that employees in the job perform. The
should state the purpose of the work and the results to be accomplished, rather than how the function is performed. Of the tasks listed , what percentage of time is devoted to each? The more time employees spend on a function, the more likely it is that the function is essential.
Generally, include those functions that account for 10% or more of the work, i.e., key items that contribute significantly to the achievement of the job. The functions should add up to 100%. % of time Essential Function (Yes/No) Key Responsibilities (To be completed by Supervisor) 30 Yes Quantitative Modeling & Simulation Lead development of PK/PD, mechanistic, Bayesian, QSP, and AI-enabled models Design predictive frameworks for TB regimen optimization Develop translational strategies linking preclinical and clinical data 25 Yes Computational Research & Data Integration Integrate multi-source datasets Develop computational workflows Apply machine learning and statistical methods 15 Yes Scientific Leadership Guide modeling strategy Collaborate with external investigators Influence scientific decision making 15 Yes Publications, Grants & Scientific Communication Manuscripts Conference presentations Grant development 15 Yes Mentoring & Technical Leadership Mentor trainees Lead interdisciplinary project teams Establish best practices 0 0 0 0 0 0 100% (To update total %, enter the amount of time in whole numbers (without the % symbol - e.g., 15, 20) then highlight the total sum (e.g., 1%) at the bottom of the column and press F9.
The total sum should add up to 100%.) Required Qualifications Bachelor's degree in Computer / Computational / Data Science, or Domain Sciences with computer / computational / data specialization or equivalent experience. Minimum 5 years relevant experience Advanced knowledge of pharmacometrics, quantitative pharmacology, statistical modeling, and computational science Demonstrated expertise in model-informed drug development (MIDD) Experience developing mechanistic, PK/PD, Bayesian, or machine learning models Advanced programming skills in Python and/or R Ability to integrate large-scale biological, clinical, and translational datasets Demonstrated scientific leadership and independent research capability Ability to communicate complex quantitative concepts to scientific and non-scientific audiences Experience managing multiple concurrent research projects
Master's degree in Computer / Computational / Data Science, or Domain Sciences with computer / computational / data specialization preferred. Postdoctoral or industry experience in quantitative drug development QSP, AI/ML Pharmacogenomics, Toxicokinetics Clinical trial simulation, Infectious disease modeling TB experience, Regulatory interactions Grant writing experience
Full time · Day shift