Science Education/Improvement Science Post-Doctoral Research Associate
Job Title: Science Education/Improvement Science Post-Doctoral Research Associate
Job ID: 2018464
Location: Storrs Campus
The ‘Multi-Level Networked Improvement Community for Supporting STEM Teaching and Learning' project at the University of Connecticut is recruiting applicants for a Postdoctoral Research Associate position. The successful applicant will play a key role on this foundation-funded research project (PI: Todd Campbell, Ph.D.) proposed to support the implementation of transformative changes in STEM teaching and learning outlined in the Next Generation Science Standards (NGSS). The project will further develop and research a multi-level, multidirectional learning and innovation Networked Improvement Community (NIC). The NIC will emerge out of initial concentrated and strategic planning to build a sustainable infrastructure connecting important groups across the complex local, regional, and national education system that can learn with and from one another. The primary research will focus on researching and codifying developing teacher knowledge and performances supportive of STEM teaching and learning across the NIC that is grounded in common theoretical ideas about how people learn and inscribed in tools that support those within and beyond the NIC. The successful candidate will join the intellectually rich Neag School of Education community and will have opportunities to collaborate with an outstanding group of national, state, district, school, and teacher leaders to learn with while contributing significantly to a developing research program.
- Ph.D. in a relevant field, such as Science Education, STEM Education, Learning Sciences, or Improvement Science.
- A record of research productivity is required.
- Strong record of engagement in NGSS implementation and research or in improvement science or research practice partnerships, as evidenced by Ph.D. thesis, and/or publications and experience.
- Experience in quantitative and qualitative research methods.
- Grant writing experience.
- Instructional experience in STEM teaching methods courses.
This will be a full-time, 12-month position with an anticipated start date of August 23, 2018. The position may be renewed for a second year pending funding and performance. Salary will be commensurate with experience. For additional information regarding benefits visit: http://hr.uconn.edu/benefits-summaries/ . For additional information about the University visit: http://www.uconn.edu/
Please apply online at www.jobs.uconn.edu, Staff Positions. Interested candidates should submit a letter of interest, a CV, up to three representative publications, and the contact information for three references. Questions regarding this position may be directed to Professor Todd Campbell via email at [email protected]. Employment of the successful candidate will be contingent on the successful completion of a pre-employment criminal background check. (Search #2018464)
This job posting is scheduled to be removed at 11:59 p.m. Eastern time on April 26, 2018.
All employees are subject to adherence to the State Code of Ethics which may be found at http://www.ct.gov/ethics/site/default.asp .
The University of Connecticut is committed to building and supporting a multicultural and diverse community of students, faculty and staff. The diversity of students, faculty and staff continues to increase, as does the number of honors students, valedictorians and salutatorians who consistently make UConn their top choice. More than 100 research centers and institutes serve the University's teaching, research, diversity, and outreach missions, leading to UConn's ranking as one of the nation's top research universities. UConn's faculty and staff are the critical link to fostering and expanding our vibrant, multicultural and diverse University community. As an Affirmative Action/Equal Employment Opportunity employer, UConn encourages applications from women, veterans, people with disabilities and members of traditionally underrepresented populations.