Applications are invited for a Postdoctoral Research Associate position to join Prof. Daniel Anglés-Alcázar’s research group in Computational Galaxy Formation at the University of Connecticut. The successful candidate will work closely with the group on research topics ranging from supermassive black hole growth and feedback to the impact of baryonic physics in cosmology. The growing UConn astrophysics group, in beautiful Storrs CT, has a vibrant and collegial atmosphere and also includes research groups on star formation, the interstellar medium, galaxy evolution, active galactic nuclei, and gravitational-wave astrophysics.
Candidates with research expertise and interests at the intersection of galaxy formation, cosmology, and machine learning are particularly encouraged to apply, with the expectation to become part of the Cosmology and Astrophysics with MachinE Learning Simulations (CAMELS) project and collaborate closely with external groups at the Flatiron Institute, Columbia, and Princeton, including two other concurrent postdoctoral hires at Princeton and Columbia. Other areas of particular focus include research on black hole-galaxy co-evolution as part of the Feedback In Realistic Environments (FIRE) project, and the new Interscale Galactic NucleI Simulations (IGNIS) resolving AGN accretion disk scales in a full cosmological context. The successful candidate will have the possibility to be a guest researcher at the Center for Computational Astrophysics of the Flatiron Institute for extended research collaborations.
Ph.D. in Astrophysics or a closely-related field by start date.
1. Interests and expertise in cosmology, galaxy formation, and/or machine learning.
2. Experience working with cosmological hydrodynamic simulations and/or large datasets.
3. Strong track record of publications.
4. A commitment to promoting an inclusive community in Astrophysics.
The expected start date is in the summer or fall of 2022. The initial appointment will be for one year, with an anticipated renewal for up to three years based on performance and availability of funds.
TERMS AND CONDITIONS OF EMPLOYMENT
Employment at the University of Connecticut is contingent upon the successful candidate’s compliance with the University’s Mandatory Workforce COVID-19 Vaccination Policy. This Policy states that all workforce members are required to have or obtain a Covid-19 vaccination as a term and condition of employment at UConn, unless an exemption or deferral has been approved.
Employment of the successful candidate is contingent upon the successful completion of a pre-employment criminal background check.
Please apply online at https://hr.uconn.edu/jobs, Staff Positions, Search #495749 to upload a resume, cover letter, publication list, and a research statement (not exceeding three pages) in a single, combined PDF file. Additionally, please arrange for three letters of reference to be sent to Prof. Daniel Anglés-Alcázar ([email protected]). Questions or requests for further information about the position should be directed to Prof. Daniel Anglés-Alcázar. Evaluation of applications will begin December 1, 2021 and will continue until the position is filled.
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.