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University of California

Two phylodynamics postdoc positions

University of California, San Francisco, California, United States, 94199


Postdoc PositionsPostdoc Positions at UCSFThe EPPIcenter and the Müller lab at UC San Francisco (UCSF) are seeking two postdoctoral research fellows in phylodynamics to study the transmission dynamics of bacterial or viral infectious diseases. The candidate will work closely with Dr. Nicola Müller and become part of the multidisciplinary research environment of the Experimental and Population-based Pathogen Investigation Center (EPPIcenter), with plenty of options for additional mentorship from other faculty. The positions are fully funded. Both positions will investigate the transmission dynamics of infectious diseases from pathogen genome data using phylodynamic methods.The exact projects will be tailored to the strengths and goals of the candidates when starting the positions. The lab will support and encourage your independent research and career interests that broadly align with the lab's interests and tailor the project accordingly. You will be expected to participate in the center's meetings and seminar series, perform novel research, publish in peer-reviewed journals, and participate in national and international conferences.The expected pay range for the position is based on experience and is anticipated to be between 66,737 and 80,034 USD per year plus benefits. The positions will remain open until filled.The Müller lab is a new group in the Division of HIV, Infectious Diseases, and Global Medicine in the Department of Medicine at the Center at UCSF. We use phylodynamics to quantify the transmission dynamics of bacterial and viral infectious diseases, ultimately to inform public health interventions. We seek to undertake cutting-edge research while valuing a strong work-life balance. As a new group, the Müller lab provides opportunities for close mentorship if desired. As a member of the EPPIcenter, you will also be part of a larger group of infectious disease researchers.You will become part of and be co-located with other members of the EPPIcenter. The center consists of multiple PIs, trainees, and both dry and wet lab spaces. We believe that diversity in backgrounds and perspectives enriches our research and encourage applications from a diverse pool of candidates. We particularly encourage members of groups that have historically been underrepresented in science to apply.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, sex, sexual orientation, gender identity, national origin, disability, age or protected veteran status.Job Requirements:PhD or equivalent in a relevant field when starting the position. Relevant fields include, but are not limited to, computational biology, statistics, epidemiology, genomics, and infectious diseases.Coding/scripting experience with high-level programming languages, such as Python, R, or Matlab.Familiarity with viral or bacterial infectious diseases.Ability to communicate your science and engage with the research of others.Helpful Skills:Background in basic biology, population genetics, or ecology.Experience in the analysis/modeling of infectious diseases.Strong statistical skills.Programming experience in Java.Experience handling viral or bacterial genome data.Prior experience in phylogenetics and/or phylodynamics.How to Apply:Please send a CV including publications, a brief statement of research/career interests (1 page), and the names, context, and contact information for three references to EPPIcenter@ucsf.edu with the subject line

'Phylodynamics postdoc'The EPPIcenter brings a systems epidemiology approach to understanding complex infectious disease dynamics by integrating state-of-the-art data collection, molecular technologies, and computational analysis. Our interdisciplinary approach provides novel insight into the targeting of interventions to reduce and ultimately eliminate infectious disease burden.

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