Experienced PKPD Modelers for scientific research in the Savic Laboratory at the University of California, San Francisco (UCSF)

The Savic Lab is seeking experienced pharmacokinetic-pharmacodynamic (PKPD) modelers with expertise in advanced complex methodologies such as data analytics, machine learning (ML) and mechanistic modeling with a background in statistics, data science, applied mathematics, pharmacometrics or related fields (e.g., pharmacokinetics, pharmacodynamics, quantitative pharmacology, systemic pharmacology, computational biology) for scientific research and scientific mentoring of junior scientists. Background: The research laboratory of Prof. Rada Savic in the Department of Bioengineering and Therapeutic Sciences of the University of California, San Francisco (UCSF) is engaged in groundbreaking drug development projects for Global Health including infectious diseases such as tuberculosis, HIV, and malaria, as well as pain management and autoimmune disease with a focus on identifying optimal treatment regimens. We develop and apply disease models, perform pharmacokinetic and pharmacodynamic evaluations, build physiology-based pharmacokinetic models, conduct exposure-response analyses, and utilize model-based simulations with the ultimate goal to optimize treatment in adult, pediatric, and special patient populations. To this aim, the Savic Lab collaborates with clinical and preclinical leaders within the field, as well as with federal an international global health partners, private foundations, and with pharmaceutical and biotech companies. The Savic Lab has a strong collegial character where passionate scientist, with different backgrounds, collaborate and share their knowledge within focused teams that enable the miracle of scientific research to make a difference. Required skills: In addition to passion for scientific research, enthusiasm, motivation and independent thinking, candidates must have knowledge of pharmacokinetic-pharmacodynamic modeling and simulation, including some advanced statistical principles (nonlinear mixed effects modelling, Bayesian statistics, clinical trial simulations) and a Ph.D. in Pharmacometrics, Biopharmaceutics, Pharmaceutical Sciences, Mathematics, Statistics, Data Science, Computational Biology, Computer Science, or related discipline with at least 3 years of experience with increasing responsibility and independence. Strong programming skills in R, Matlab or Python and extensive experience in modeling and simulation software such as NONMEM, Monolix, Phoenix NLME, PKsim, and/or SimCYP is essential. Knowledge of drug development, high-performance computing, and dynamic modeling are preferred. Personal skills such as teamwork, accurate listening, strategic thinking, along with very good oral and written English language skills is expected and will be critical for the successful candidates. Salary and benefits is commensurate with experience. Submit E-mail a curriculum vitae, a letter stating research interests and contact information for three references to the link: https://jobs.brassring.com/TGnewUI/Search/home/HomeWithPreLoad?PageType=JobDetails&partnerid=6495&siteid=5861&Areq=77706BR Feel free to submit any question to: [email protected]<mailto:[email protected]>. UC San Francisco seeks candidates whose experience, teaching, research, or community service that 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, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status.