Qualifications:
Required: Applicant must hold a Ph.D. in a relevant subject (e.g. in a data-intensive subfield of Biology, Ecology or Environmental Science) or in computer science (with publications in one of these subfields). It is essential that the applicant have experience working with large-scale data sets including application of data processing pipelines and machine learning models. The applicant also must have experience with acquiring data through crowdsourcing or citizen science methodologies. 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 an Ecology research context, for example, with data generated from camera traps. Preference will also be given to applicants who have a demonstrated familiarity with data management and image processing tools; a demonstrated interest in citizen science; the ability to work independently and manage multiple projects; excellent organizational, presentation and writing skills; and demonstrated self-motivation and creativity.

The Zooniverse team in the School of Physics and Astronomy at The University of Minnesota has an opening for a Researcher 5 position with a focus on applications in Ecology that combine both citizen science and machine learning techniques. Through volunteer classifications, Zooniverse projects use the combined input from a large number of imperfect classifiers to enable knowledge discovery from large-scale datasets across the domains. Under the onslaught of even more data and as machine algorithms continue to improve, the Zooniverse team is investigating approaches that optimally combine human and machine classifiers. Zooniverse platform infrastructure has been implemented to enable this combined approach with great success in the Ecology domains. Funding has been obtained to explore applications of this combined approach in solving the problem of identifying individual animals (re-identification) in image or video captures. In addition, infrastructure has been developed to connect the Zooniverse API with the CitSci.org API to enable photo uploads to the CitSci.org platform to be ingested into a Zooniverse project. Furthermore, a collaboration between the Zooniverse and Wildlife Insights was recently funded to connect their APIs allowing for the easy transmission between the two platforms of relevant data sets and metadata including Deep Neural Network labels and crowd classification results for those research teams who want to exploit the benefits of each platform.

The successful applicant will work on several tasks related to the management of Zooniverse ecology projects with a focus on prototyping, testing, documenting and collaborating with team members on improving new Zooniverse infrastructure that (1) combines machine and human classifiers; and (2) connects the Zooniverse platform with others such as CitSci.org or Wildlife Insights. The applicant will also be responsible for developing and leading tutorials and workshops to train other researchers on these new tools. Specific duties will also include testing existing algorithms used for current camera trap projects on Zooniverse such as Snapshot Safari, and collaborating on advancing the capabilities of the combined human-machine system to improve counts, behaviors, demographic data, and recognition of rare and endangered species as well as mounting experiments using crowd labels and machine learning to enable individual animal identification.

The Researcher 5 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 Ecology, several data science post-docs working in astronomy, medical imaging and digital humanities, and a dedicated Zooniverse web developer. The successful applicant would also 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. Additionally, the Researcher 5 would work closely with both the CitSci.org and Wildlife Insights teams aiming to assist other research teams identified in using the data science infrastructure on both platforms. The Researcher 5 would be expected to collaborate scientifically with ecology researchers using data from the Snapshot Safari projects and work towards the publication of ecology research exploiting the data obtained through these Zooniverse projects. The successful applicant would be expected to work with and, in some instances, mentor undergraduate or graduate students from a range of domains including the UMN Data Science Masters program or UMN’s Department of Ecology, Evolution and Behavior with assistance from several faculty and staff in the Informatics Institute at UMN who are engaged in Zooniverse projects. The position is grant-funded for one year with the possibility of continued funding if further grants are successful.

Responsibilities/duties

30% Manage Zooniverse-CitSci.org and -Wildlife Insights Integration Work with both CitSci.org and Wildlife Insights collaborators and ecology research teams to implement and test outcomes of initial projects using the combined platforms. This could include help in preparation of meta-data, processing of data through existing ecology ML models, assistance with setting up related Zooniverse projects and the aggregation of the ensuing crowdsourced data. Assist as needed with retraining and testing of existing algorithms used for current ecology projects on Zooniverse.

30% Zooniverse Experiments: Lead the implementation of experiments combining machine learning and volunteer classifiers on Zooniverse ecology projects, in particular with Snapshot Safari teams, with the two objectives of (a) advancing the capabilities of existing algorithms to improve counts, behaviors, demographic data, and recognition of rare and endangered species; and (b) contributing to a solution for individual animal identification in these projects.

20% Ecology Research: carry out a research program and communicate results on at least one of the above efforts via academic publication in appropriate journals. Mentor graduate and undergraduate students who are tasked with developing, implementing and analyzing aspects of the above effort.

20% Communication, dissemination and development: Take the initiative to keep all project stakeholders informed of progress or concerns. 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. Develop and present workshop and tutorial materials to guide research teams on use of new Zooniverse tools related to ecology. Represent the Zooniverse team at meetings and conferences as required. Take a leadership role in group meetings and discussions on strategy and development. Assist in drafting proposals to procure further research funding. 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.

School of Physics and Astronomy https://cse.umn.edu/physics

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