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Postdoctoral Appointee - Ecology and Machine Learning

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Job Details
Job Order Number
Company Name
Argonne National Laboratory
Physical Address

Lemont, IL 60439
Job Description

Postdoctoral Appointee \- Ecology and Machine Learning

Requisition Number: 406433 Location: Lemont, IL

Functional Area: Research and Development Division: EVS\-Environmental Science

Employment Category: Temporary 6 Months or Greater Education Required: Not Indicated

Level \(Grade\): 700 Shift: 8:30 \- 5:00 Share: Facebook LinkedIn Twitter

The Environmental Science Division of Argonne National Laboratory seeks a postdoctoral researcher to contribute to a project investigating how soil moisture heterogeneity influences the exchange of energy, water and carbon between terrestrial ecosystems and the atmosphere\.

In this role you will:

+ Contribute to the conceptual design of terrestrial and atmospheric measurements needed to test the effects of temporal and spatial heterogeneity of soil moisture in mid continental temperate landscapes\.
+ Contribute to the installation and operation of field instruments\.
+ Contribute to data model development and application of data analysis methods, including machine learning and information theory based approaches\.
+ Team with soils scientists, microbial ecologists, atmospheric scientists and computational scientists, and supervise students\.
+ Present results at scientific meetings, and prepare manuscripts for publication\.

We expect you to have knowledge and/or skills in:

+ Knowledge of terrestrial ecology and ecosystem processes and a willingness to learn of hydrology or atmospheric processes OR knowledge of hydrology, surface atmospheric processes and a willingness to learn terrestrial ecology and ecosystem processes\.
+ Experience with relevant field instruments and methodologies, such as eddy flux towers, flux chamber measurements, soil sensors, or other field instruments\.
+ Experience in working with large datasets and strong data analysis skills, and knowledge of or interest in statistical methods and /or familiarity with machine learning or information theory based approaches\.
+ Strong writing and oral communication skills\.
+ Strong organizational skills and the ability to coordinate the activities of technical support personnel and undergraduate interns\.
+ Demonstrated ability to work independently and in a team environment\.
+ Candidates should have a recent Ph\.D\. in terrestrial ecology, biogeochemistry, ecosystem science, soil science, ecohydrology, atmospheric science, or related field\.

As an equal employment opportunity and affirmative action employer, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation\. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment\. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other characteristic protected by law\.

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