National Science Foundation Announces First Round of NRT Interdisciplinary Training Grants

April 27, 2015

NSF has just awarded the first round of new grants under the new National Science Foundation Research Traineeship (NRT) program (see the press release). NRT is the successor the long-running and highly successful Interdisciplinary Graduate Education, Research, and Training (IGERT) program. Over 17 years, starting in 1998, IGERT funded 303 programs at universities across the United States involving hundreds of faculty and almost 9,000 graduate trainees. With the launch of the NRT program last year, NSF has refocused its investments in interdisciplinary graduate training to acheive several goals, including focused funding on national research priorities, expanded training of master’s and PhD students, and an emphasis on training that prepares students for a wide range of professional careers in and out of academia.

In the first rounds of funding, NSF identified data sciences as a priority theme. The eight funded programs span a wide range of topics:

  • NRT-DESE*: GAUSSI: Generating, Analyzing, and Understanding Sensory and Sequencing Information–A Trans-Disciplinary Graduate Training Program in Biosensing and Computational Biology (Colorado State)
  • NRT-DESE: Computational Materials Education and Training – Bridging ab initio Methods and Applications (COMET) (Penn State)
  • NRT-DESE: Training in Data-Driven Discovery – From the Earth and the Universe to the Successful Careers of the Future (Northwestern)
  • NRT-DESE: Graduate Training in Data-Enabled Research into Human Behavior and its Cognitive and Neural Mechanisms (Rochester)
  • NRT: Education Model Program on Water-Energy Research (EMPOWER) (Syracuse)
  • NRT: Training Next-Generation Scientists with Experimental, Theoretical, and Computational Competencies for Complex Interfaces (INTERFACE) (Southern Mississippi)
  • NRT-DESE: Flexibility in Language Processes and Technology: Human- and Global-Scale (Maryland College Park)
  • NRT-DESE: Environment and Society: Data Science for the 21st century (DS421) (UC Berkeley)

*DESE indicates programs funded under the "Data Enabled Science and Engineering" theme

The last one on the list—DS421is a new program here at UC Berkeley getting underway this year with the first cohort of students starting in fall 2015. DS421 addresses grand challenges at the intersection of natural and social sciences related to rapid environmental change in the 21st century with a focus on the new tools and opportunities provided by data sciences. As a training grant, our goal is to equip students with conceptual and technical tools to think outside the box and work across disciplines in development of their dissertation research and to build a community of students and faculty who can work together to break down disciplinary and departmental barriers. Research challenges focus on the dynamics of coupled human-natural systems in a rapidly changing world.

The DS421 program was designed by faculty from eight departments and five colleges (Letters & Sciences, Natural Resources, Engineering, Public Policy and Environmental Design), including four BIDS Senior Fellows (Ackerly, Culler, Kelly, and Stark). DS421 will help advance graduate training goals aligned with the BIDS mission, and students will be active participants in BIDS workshops and other activities.

Students are invited from any department at UC Berkeley, and the 2015 cohort includes students from Agricultural and Resource Economics; Environmental Science, Policy, and Management; Energy and Resources; Integrative Biology; Statistics; Goldman School of Public Policy; Landscape Architecture and Environmental Planning; and Civil and Environmental Engineering.

For more information, see the program website: ds421.berkeley.edu



Featured Fellows

David Ackerly

Integrative Biology

David Culler

Electrical Engineering & Computer Sciences
Co-I for Moore/Sloan Data Science Environments

Maggi Kelly

Environmental Science, Policy, & Management; Geospatial Innovation Facility

Philip Stark

Statistics; Statistical Computing Facility
Co-I for Moore/Sloan Data Science Environments