Postdoctoral scholar for disease ecology, spillover, and AI
Strategies are needed to identify and characterize viruses that pose the greatest risk of spillover to humans and their relative pandemic potential to inform vaccine targets. We identified risk factors contributing to virus spillover to humans, and then developed a risk ranking framework and interactive web tool, SpillOver, that estimated a risk score for wildlife-origin viruses. Awareness of the dearth of information available on emerging viruses has stimulated viral discovery work around the world. Those data are already becoming accessible, and huge volumes of new information will almost certainly be imminently available on newly-recognized viruses that can and should be incorporated into a risk-ranking framework. There is an ongoing need to constantly update data and risk rankings to incorporate new discoveries in real-time, and artificial intelligence could be incorporated into the SpillOver platform to make sure that new discoveries are ranked in near real-time.
The Office of Grand Challenges provides dedicated leadership and facilitation for current and future research, education, and public service activities focused on UC Davis campus priorities for action to address the world’s wicked problems (herein “Grand Challenges”), including, but not limited to: emerging health threats, the climate crisis, reimagining the land grant university, and sustainable food systems. Grand Challenges is also planning a new built environment, conceived of as a special space that will be an ideal site for Grand Challenges programs and a focal point for transdisciplinary collaboration campus-wide.
Under general direction of the Grand Challenges Vice Provost, the postdoctoral scholar will support strategically important projects, for e.g. SpillOver, within the UC Davis Institute for Pandemic Intelligence (IPI) involving computational methods by designing analytical models and implementing them in operational and research environments.
The core duties of the postdoctoral scholar will be to update the current SpillOver Program using Artificial Intelligence (AI) and other methods to increase the breadth of data incorporated in virus risk rankings and constantly update and optimize the rankings with the large volumes of new data becoming available from viral discovery efforts to inform vaccine target selection. They will be responsible for literature and dataset review and development of innovative methods for data analytics and data visualization, ongoing assessment of data gaps, and making recommendations to improve data quality and analyses. Development of peer-reviewed publications and conference presentations on updated risk rankings and high-risk viruses, based on the expanded information collected through artificial intelligence, and recommendations on updated international standards for viral risk rankings will be key.
• Contribute intellectually to the SpillOver team, including identification of subprojects and products of interest that contribute to the candidate’s career trajectory and the team’s goals and success.
• Provide data science innovation for the project, including data acquisition, cleaning, visualization and analyses.
• Develop, maintain, and document analytical models (including machine learning and other AI algorithms, spatially explicit modeling, mixed models, Bayesian methods, etc.) and software applications for synthesizing and updating SpillOver data.
• Identify high-quality sources of data relevant to zoonotic viruses and automate processes for their collection, cleaning, and analyses to maintain updated virus risk rankings.
• Provide analytical facilitation and project management support for matters relating to the day-to-day activities of the project, including potential stewardship of research personnel, educational teams, funders, and other stakeholders.
• Communicate and work with collaborators with different expertise and domain knowledge.
• Assist in preparing materials for high-level conversations and lead academic publications.
Basic qualifications (required at time of application)
• PhD in a field related to Data Science (Computer Science, Statistics, Artificial Intelligence, Information Science, etc.) OR in an applied domain with a research background that contains a significant emphasis on quantitative approaches and skillsets including data analysis and scientific computing.
• Proficiency in R and/or Python.
• Experience and interest in AI-driven applications.
• Experience in hands-on data science problem solving with real-world, complex data sets.
• Competency with code management best practices including version control (git) and reproducible analyses.
• Fluency in written and spoken English and strong written and verbal communication skills.
• Ability to work both independently and as part of an interdisciplinary collaborative team.
- University of California, Davis
- Closing date
- February 28th, 2023
- Posted on
- January 20th, 2023 23:42
- Last updated
- January 20th, 2023 23:42