Assoc./Full Specialist -Fire Science/Forest Ecology Department of Environmental Science, Policy, and Management

The Forest Ecology Lab, under the guidance of Dr. John Battles (PI), with the Department of Environmental Science, Policy, & Management (ESPM) within the Rausser College of Natural Resources (RCNR) at the University of California, Berkeley, is seeking highly motivated individuals for the position of Associate /Full Specialist.

The Forest Ecology Lab is a group of scholars who share an interest in understanding forest ecosystems in terms of their ecology, management, and policy. Robust, quantitative field studies form the core of our approach. Specifically, we want to know how and why forests change. Answering these questions is more than just an interesting academic puzzle. We expand the scope of our field-based insights by collaborating with a diverse group of ecosystem scientists, remote sensing experts, and data scientists to develop scientifically robust and management relevant information. Specifically, we work with scientists, managers and policy makers in California to develop the best available information to reduce the vulnerability of our forests, firefighters, and communities to the risks of high severity wildfire.

A long-term, focal area for the Lab is understanding the fate of the big-tree forests of the southern Sierra Nevada under a non-stationary disturbance regime. These forests are at risk of wildfire-driven conversion to shrub-dominated landscapes. The combination of a warming climate and a legacy of fire suppression produces wildfires that burn at severities well outside the historical norm. Such fires threaten the persistence of even the most fire-adapted species, including monarch giant sequoia trees.

Our research is conducted in collaboration with co-investigators (co-I) from the United States Geological Survey and from Sequoia-Kings Canyon National Parks. These collective efforts have produced an extraordinarily rich collection of field and remote sensing data. We seek to leverage these investments to complete the following objectives:

1. Build a predictive model linking ground and surface fuels to remote sensing data in Sierran conifer forests with sufficient accuracy to inform fire and forest management decisions.

2. Develop statistical models that measure the fine-scale spatial heterogeneity of fuels at a resolution required by the next-generation of fire behavior models.

3. Quantify the sensitivity of wildfire behavior to the spatial distribution of fuel loads.

The expectation is that the incumbent will provide advanced analytical support to the research team in their efforts to quantify fuel loads and speed the development of more accurate wildfire behavior models. A unique feature of this recruitment is the opportunity to participate as a partner in the Schmidt Center for Data Science and Environment (DSE). The partner would be included in DSE's growing community and benefit from being able to seamlessly communicate, share common space, and engage in internal DSE activities, and have regular opportunities to trouble-shoot and bounce ideas off DSE Staff.

Specific responsibilities include:

1. In consultation with the PI and co-I's, develop robust data analysis plans that integrate remotely sensed and field inventory data using appropriate statistical methods.

2. Evaluate model results in terms of their ability to produce management relevant information at the appropriate spatial and temporal scale.

3. Interpret and communicate applied research insights in peer-reviewed manuscripts, reports, and outreach material

4. Build open-science workflows that can be shared among collaborators.

5. Conduct all research and analyses following best practices in reproducible science.

6. Communicate technical program details effectively with a diverse group of fire scientitsts, resource managers, and forest ecologists to ensure understanding and engagement with domain experts.

7. Contribute expertise to the environmental data science workgroup at UC Berkeley

Master's degree (or equivalent international degree) or enrolled in a Master's degree (or equivalent international degree) program.

At least three years of experience in environmental statistics and/or data science is required at the time of appointment.

A master's degree in a relevant discipline (e.g., fire science, forestry, ecology, natural resource management, data science, statistics).

Two years of experience in a relevant domain discipline (e.g., remote sensing, fire science, forest ecology, forest management).

Experience with Bayesian hierarchical modeling and geospatial statisitics.

Experience working with large data sets that involve remote sensing and forest inventory data.

Demonstrated ability to work effectively in large interdisciplinary research teams.

Demonstrated capacity to publish peer-reviewed articles or non-peer reviewed reports .

• 3 required (contact information only)

UC Berkeley is committed to diversity, equity, inclusion, and belonging. The excellence of the institution requires an environment in which the diverse community of faculty, students, and staff are welcome and included. Successful candidates will demonstrate knowledge and skill related to ensuring equity and inclusion in the activities of their academic position (e.g., teaching, research, and service, as applicable).

The University of California, Berkeley is an Equal Opportunity/Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, or protected veteran status.

Please refer to the University of California's Affirmative Action Policy and the University of California's Anti-Discrimination Policy.

In searches when letters of reference are required all letters will be treated as confidential per University of California policy and California state law. Please refer potential referees, including when letters are provided via a third party (i.e., dossier service or career center), to the UC Berkeley statement of confidentiality prior to submitting their letter.

As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements.