The Zooniverse team in the School of Physics and Astronomy at The University of Minnesota has openings for three Research Associate positions; two focused on machine learning and data science applications in Astrophysics; one focused on machine learning and data science applications in Medical Imaging and Humanities. This job description is for the lead Astrophysics position. We seek a research associate with strong experience in data science and machine learning to further develop the world’s largest platform for citizen science. Zooniverse uses the combined input from over 2 million volunteer classifiers to provide labeling and other tasks across hundreds of research projects in a variety of domains. Under the onslaught of ever-larger amounts of data and to take advantage of improvements in machine algorithms, the Zooniverse team has built and implemented infrastructure to enable novel approaches that optimally combine human and machine classifiers. We seek individuals who are excited by the idea of using this new infrastructure on data-intensive sub-fields of Astrophysics with the goal of most efficiently classifying “known-knowns” while at the same time enabling the serendipitous discovery of completely new classes of objects in a given astrophysical dataset.
The research associate will be supervised by Lucy Fortson, faculty member in Physics and Astronomy, co-founder of Zooniverse and director of the Zooniverse effort at UMN. The UMN Zooniverse effort comprises science team members across multiple Zooniverse projects including many in Astrophysics, several data science research associates and students working in astronomy, ecology, medical imaging and digital humanities, and a dedicated Zooniverse web developer. The successful applicant would work closely with Zooniverse team members at the Adler Planetarium in Chicago and the University of Oxford, UK who are guiding and developing the Zooniverse platform infrastructure to combine human and machine classifiers; there is a budget for several collaborative visits to these locations for this project. Additionally, the research associate would be expected to work with and directly mentor graduate students from the UMN Data Science Masters program as well as undergraduates from a range of domains with assistance from several faculty in Computer Science and the Informatics Institute at UMN who are engaged in Zooniverse projects. The Zooniverse uses the best technology available to provide cutting-edge tools to scientists using our platform; we expect the results produced by the research associates to be adopted by the team and deployed in production. The position is grant-funded for two years with the possibility of continued funding if further grants are successful.
45% Zooniverse Experiments: Lead the implementation of experiments carried out on the Zooniverse platform to study the impact of combining human and machine classifiers on overall classification efficiency as well as optimizing for serendipitous discovery. While focused on the astrophysics domain, these experiments might use other domains for comparisons. Analyze the data from these experiments and carry out further experiments as needed. Publish findings in appropriate journals. Use results from the experiments to make recommendations on infrastructure development, and work with the development team to ensure such strategies are implemented successfully. 20% Astrophysics Research: carry out a research program exploiting astrophysics-related data sets available to the research associate through Zooniverse and communicate results via academic publication in appropriate journals. 20% Manage and Coordinate UMN Zooniverse Data Science Efforts Work with UMN collaborators and research teams to consult on the design of Zooniverse projects with a specific focus on data science needs. This includes guidance in preparation of meta-data, processing of data through existing ML models, assistance with setting up related Zooniverse projects and the aggregation of the ensuing crowdsourced data. In consultation with Fortson, take a leadership role in overseeing the data science team at UMN through active engagement in individual and group meetings, setting workplans and assigning tasks to achieve target deliverables, and monitoring and adjusting milestones as needed. Mentor graduate and undergraduate students who are tasked with developing, implementing and analyzing aspects of the group effort. Assist wider Zooniverse team by contributing to key service tasks such as general project review, and participation in relevant working groups. Take a leadership role in wider Zooniverse team meetings and discussions on strategy and development. 15% Communication, dissemination and development: Take the initiative to keep all project stakeholders informed of progress or concerns. Represent the Zooniverse team at meetings and conferences as required. Work with external collaborators to understand the requirements imposed on the Zooniverse system by the needs of scientists who are making use of it and document these needs. Contribute as appropriate to project reports to granting agency. Maintain professional development, trying out and becoming familiar with new technologies through individual initiative as well as through drawing on collaboration and support from team members.
Other duties of a similar scope as assigned.
Qualifications: Required: Applicant must hold a Ph.D. in a relevant subject (e.g. in a data-intensive field such as Physics or Astronomy) or in computer science. It is essential that the applicant have demonstrated experience with a set of tools appropriate for working with large-scale data science including application of machine learning. A strong publication record in relevant academic field(s) is also required as is the ability to mentor students and work in a diverse, distributed team in an interdisciplinary manner with an ability to direct one’s own research.
Preferred: Preference will be given to applicants who have experience implementing machine-learning algorithms in a research context in either academia or industry as well as demonstrated familiarity with classifier combination problems or with research into human-computer systems; a demonstrated interest in citizen science; the ability to manage multiple projects; experience in managing working groups or small teams; excellent organizational, presentation and writing skills; and demonstrated self-motivation and creativity. While based in Minneapolis, the successful applicant will be expected to travel to Chicago and Oxford, UK.
Internal Number: 338067
About University of Minnesota, Twin Cities
The University of Minnesota, founded in the belief that all people are enriched by understanding, is dedicated to the advancement of learning and the search for truth; to the sharing of this knowledge through education for a diverse community; and to the application of this knowledge to benefit the people of the state, the nation, and the world.