Postdoctoral Researcher: Data Science, Earth Science with a Cryosphere emphasis and Open Source Software
Supervisor: Dr. Fernando Pérez (@fperez), UC Berkeley Department of Statistics.
I am looking for a post-doctoral researcher interested in the intersection of geoscience (with a particular emphasis on cryosphere science and glaciology), data science, and open source computational tools to join our group in the Department of Statistics at UC Berkeley. You would be joining a multi-disciplinary team of researchers with backgrounds in statistics and geoscience as well as folks leading open-source projects in the Jupyter community. To give you a flavor of a few projects that we have on the go:
Jupyter meets the earth: we are using research avenues in climate science, hydrology, geophysics and cryosphere science to motivate technical developments within the Jupyter ecosystem. We are aiming to strengthen the cryosphere science uses of these tools, including a partnership with the team at the University of Washington that leads the development of the IceSat-2 HackWeeks. We aim to jointly contribute to the cryosphere science community by providing better tools for large-scale data-driven research in an interdisciplinary setting, and to inform the evolution of Jupyter thanks to these concrete use cases in scientific research, education and collaboration.
Glaciology / cryosphere science: This is a current focus of several of the graduate students working with me and is an area I am very interested in making contributions to. In addition to the above, we are connected with collaborators at the University of Washington on a project to develop software for working with ICESat2 data (icepyx). We are also initiating collaborations with colleagues at the School of Mines to look at machine learning approaches for estimating surface-water volumes through time on Greenland and Antarctica. We would welcome contributions to these projects as well as new ideas or other avenues of research in the cryosciences.
Physics & Machine Learning: I am interested in questions that are at the intersection of physically-driven models and statistical learning. This is a general theme throughout the projects we are pursuing, and I would welcome the opportunity to work with someone who is interested in this intersection, especially as these ideas are expressed in geoscience applications.
An ideal candidate would
- Have a PhD in a field such as (Geo)Physics, Earth and Planetary Science, Statistics, Data Science or related.
- Have experience and interest in mentoring PhD students in the group.
- Have demonstrated leadership in collaborative projects.
- Be self-directed on broadly-scoped projects.
- Be proficient in Python or R and tools in the open-source data science ecosystem.
- Be willing to contribute to grant proposal writing.
Location-wise it is preferable if the candidate can work locally at Berkeley, but I am open to considering remote options, especially at the start (and given the coronavirus uncertainty, that may even be required at least for a while).
If you are interested, please provide:
- A Curriculum Vitæ
- And a short (1-2 page) statement describing your research interests and how you envision contributing to the projects described above
By email to Fernando Pérez and Lindsey Heagy (firstname.lastname@example.org, email@example.com).