Postdoctoral Fellow - Davis College of Agriculture, Natural Resources and Design

Purpose
The incumbent serves as a resource to contribute machine learning and artificial intelligence capacity to a variety of ongoing research activities of the Alliance for Regenerative Livestock. The Alliance uses a variety of experimental approaches to collect autonomous, remotely sensed data about livestock production to develop predictive algorithms to assess plant and animal growth and performance. The goal is to both innovate technology and approaches to allow measurement of animals grazing grasslands at scale and to incorporate greenhouse gas production and co-location of renewable energy associated with those grazing activities. We have a goal to begin to use the approaches developed to test the role of livestock in mitigating carbonization of the atmosphere, addressing the ecological footprints (water, carbon, environmental, etc.) of livestock systems to upcycle nutrients from grasslands, the production of climate-tolerant forage cultivars, development of more regenerative livestock production systems and more.

Duties & Responsibilities
Responsibilities are functional in nature, and performed under limited supervision. Specific tasks include, but are not limited to:
1) Plan and conduct analyses of large datasets in support of various research questions.
2) Create and maintain existing project databases for data analysis.
3) Attend weekly, monthly, quarterly and yearly meetings as needed with project partners.
4) Attend research conferences and read scientific literature to keep abreast of technological advances and current research findings.
a. Identify and assist with acquisition of funding from public, private, and governmental sources. Write research proposals, papers, and abstracts.
5) Conduct literature reviews as needed to meet research goals.
6) Perform laboratory and field experiment activities according to protocol.
7) Write and present research/study status reports for review and use of the Principal Investigator.
8) Prepare results of experimental findings for presentation to colleagues and sponsors; at professional/technical conferences; and/or, for publication in scientific journals, as directed.

Behavioral Competencies

1) Technical skills - Strives to continuously build knowledge and skills; pursues training and development opportunities; assess strengths, weaknesses, and shares expertise with others.
2) Problem solving - Gathers and analyzes information, identifies problems, develops alternatives, uses reason even when dealing with emotional topics and works well in groups and resolves matters timely.
3) Interpersonal Interactions in the Workplace - Manages difficult or emotional matters with objectivity and openness; responds promptly to needs and requests for service and assistance; meets commitments and dates, notifies others if change occurs; provides, solicits and applies feedback; and builds goodwill through balanced contributions, positive approach, respect and support of others.
4) Communications - Demonstrates group presentation skills, participates in meetings, listens and gets clarification, is responsive and speaks clearly and persuasively in positive and negative situations. Writes and edits clearly and informatively, varies style to meet needs, presents numerical data effectively, and able to read and interpret written information.
5) Organizational Support - Aligns work and develops strategies to meet organizational goals; seeks to improve and promote quality; demonstrates accuracy and thoroughness; and upholds organizational expectations and values.
6) Self-Management - Responds to management direction; resourcefully adapts to changes in work environment and circumstances; responds well under pressure and manages competing demands; takes calculated risks, asks for and offers help when needed; exercise discretion and sound judgment; is tactful and responsible for own actions. Adheres to work schedule and complies with leave management responsibilities.

Education and/or Experience
1) Successful completion of a Ph.D. Program from an accredited university.
2) Experience with machine learning/artificial intelligence/big data analytics is required.
3) Experience with life cycle analysis is desirable but not required.

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