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Data Science Research Positions at the University of Washington
Please note: Post-doc applications for the 2014-2105 academic year are now closed. Thank you for your interest.
The University of Washington eScience Institute is pleased to announce multiple new research positions as part of a multi-year Data Science Environment (DSE) partnership between UW, UC Berkeley, and NYU funded jointly by the Gordon and Betty Moore Foundation and the Alfred P. Sloan Foundation. Read the press release
Data Science Fellows
Driven by rapid advances in our ability to acquire and generate data, all fields of discovery are transitioning from “data-poor” to “data-rich.” The bottleneck to discovery is becoming our ability to perform inference over heterogeneous, noisy, and often massive datasets. In order to remain at the forefront, universities must lead in advancing the techniques and technologies of data-intensive discovery, and in making these techniques and technologies accessible to researchers in the broadest imaginable range of fields. The Moore and Sloan Foundations have joined with UW, Berkeley, and NYU in a collaborative experiment intended to transform the process of discovery and the institutional environments in which discovery takes place.
The UW eScience Institute is a team of interdisciplinary researchers committed to advancing these goals. Our strategy is to focus on joint programs involving methods researchers (in computer science, statistics, and applied mathematics) and domain science researchers (in the life, physical, and social sciences.)
We attract and promote top-notch interdisciplinary researchers and practitioners with backgrounds in both methods and domain science, but who may not fall neatly into conventional career paths. The successful candidates will join our team and engage in research that will advance both domain science and data science methodology: new platforms, new algorithms, new methods, and new applications. The candidates will also have opportunities, if desired, to teach short-course bootcamps, full undergraduate and courses, and will develop and run massively open online courses.
There are three types of positions available: Data Scientist, Research Scientist, and Postdoctoral Fellow. Here's how they are distinguished:
(3 positions available)
(4 positions available)
(4 positions available)
|Full support from the DSE||Partial support from the DSE, partial support from other specific research projects.||Full support from the DSE|
|Research position, with opportunities to publish but with emphasis on alternative metrics of impact (software uptake, etc.)||Research position, with emphasis on publications as well as alternative metrics||Research position, with emphasis on publications as well as alternative metrics|
|Intended for those seeking technical research career paths, not necessarily faculty positions||Intended for those seeking technical research career paths, not necessarily faculty positions||Intended for those seeking faculty positions|
|Works independently, with direction from DSE leadership||Works independently, with direction from DSE steering committee and other stakeholders commensurate with funding sources||Works independently, with direction from a team of interdisciplinary faculty advisors|
|Advanced degree may be typical, but not necessarily required||Advanced degree may be typical, but not necessarily required||Ph.D. required|
|Permanent space in the interdepartmental data science studio||Part-time space in the interdepartmental data science studio||Permanent space in the interdepartmental data science studio|
|Emphasis on short-term “incubator” projects, often contributing to shared cyberinfrastructure with hundreds of users.||Emphasis on longer-term projects with specific science goals, and “translation” between domains||Emphasis on longer-term projects with specific science goals, and “translation” between domains|
|Emphasis on alternative metrics and software uptake, with opportunities to publish||Emphasis on publications as well as alternative metrics||Emphasis on publications as well as alternative metrics|
|May be PI on grants, depending on experience||May be PI on grants, depending on experience||May be PI on grants, depending on experience|
Data Science Graduate Researchers
In addition, multiple graduate student positions are available as part of a new National Science Foundation IGERT (Integrative Graduate Education and Research Traineeship) award for an interdisciplinary Ph.D. program on data science to the same group of PIs. Read more about these positions.
While a broad range of University of Washington faculty are participating in these efforts, the principals responsible for the Moore/Sloan collaboration and the NSF IGERT award are:
|Cecilia Aragon, Human-Centered Design & Engineering||Bill Howe, Computer Science & Engineering|
|Ginger Armbrust, Oceanography||Željko Ivezić, Astronomy|
|Magda Balazinska, Computer Science & Engineering||Ed Lazowska, Computer Science & Engineering|
|David Beck, Chemical Engineering||Randy LeVeque, Applied Mathematics|
|Josh Blumenstock, Information School||Tyler McCormick, Statistics and Sociology|
|Andy Connolly, Astronomy||Marina Meila, Statistics|
|Tom Daniel, Biology||Bill Noble, Genome Sciences|
|Mark Ellis, Geography||Thomas Richardson, Statistics|
|Emily Fox, Statistics||Werner Stuetzle, Statistics|
|Dan Grossman, Computer Science & Engineering||Ben Taskar, Computer Science & Engineering|
|Carlos Guestrin, Computer Science & Engineering||Luanne Thompson, Oceanography|
|Jeff Heer, Computer Science & Engineering||John Vidale, Earth & Space Sciences|
Also in... What We Do
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Please help us support your research by including the following acknowledgment in publications to which we have contributed:
Supported in part by the University of Washington eScience Institute.