Research Scholar - Crop and Soil Sciences

Job description

Posting Number:

PG190687EP

Internal Recruitment :

No

Working Title:

Research Scholar - Crop and Soil Sciences

Anticipated Hiring Range:

Commensurate with Experience

Work Schedule:

Monday - Friday, 8am - 5pm

Job Location:

Beltsville, Maryland

Department :

Crop and Soil Sciences

About the Department:

Crop & Soil Sciences is a Department with diverse disciplines that encompass research, extension and teaching related to soil science and theory and the genetic improvement, production and utilization of agronomic and turf-grass crops. Leaders in research and applied science and the engagement and technology transfer to public and/or private sector practitioners.

Essential Job Duties:

The Research Scholar will provide remote sensing, geospatial statistics, and machine vision and learning support the Precision Sustainable Agriculture (PSA) Network and numerous partners. The candidate will work with a team of applied agricultural scientists, technologists, and data scientists to assess nitrogen dynamics, crop water stress and cover crop and weed dynamics in agronomic cropping systems. The candidate will develop empirical models, process-based models, and construct decision support tools using field data and through use of remote sensing systems (satellite, airborne, drone, tractor-mounted, field deployed). Additional activities could include conducting spatio-temporal analysis of nutrient and water dynamics in cover crop-based field crops and expanding decision support tools to have geospatial recommendation systems to interact with precision agriculture technologies.
Specific activities will include developing remote sensing and direct measures of cash and cover crops (nutrients, biomass, yield) and soils (sensor-based water content, EC, temperature measurements), as well as the using of remote sensing (satellites, drones), and proximal sensing (tractor-based LIDAR, radar, multispectral/hyperspectral cameras).

The Research Scholar will also support the deployment, quality control, and analysis of a diverse set of remote sensing approaches to quantifying water and nitrogen dynamics, and cover crop and weed performance in field crop production systems across a distributed network of researchers in the US. Conduct geospatial analysis of cover crops that link a range of remote sensing resolutions with field destructive samples and will explore linkages between sensing systems for plant growth to process-based modeling approaches and participate in decision support tool development. Deploy IoT technologies automating data acquisition and integration from a wide variety of sensors. The candidate will identify, plan and execute on opportunities to use hardware and software to automate R&D operations and increase field efficiencies. The position will also perform other duties as assigned.

The position is based in Beltsville, MD.

Other Responsibilities:

The Research Scholar is also responsible for supporting the deployment, quality control, and analysis of a diverse set of remote sensing approaches to quantifying cover crop and weed performance in field crop production systems across a distributed network of researchers in the US. The successful candidate will conduct geospatial analysis of cover crops that link a range of remote sensing resolutions with field destructive samples and will explore linkages between sensing systems for plant growth to process-based modeling approaches and participate in decision support tool development. The Research Associate will deploy IoT technologies automating data acquisition and integration from a wide variety of sensors. The candidate will identify, plan and execute on opportunities to use hardware and software to automate R&D operations and increase field efficiencies.

The position will also perform other duties as assigned.

Minimum Experience and Education:

  • PhD in applied agricultural sciences, spatial statistics, data science, machine learning, computer vision, remote sensing or related field, or a Master’s degree in statistics, applied agronomy/ecology, computer science, machine learning and at least five (5) years of experience.
  • Candidates should have training and experience with computer vision algorithms and libraries, machine learning for pattern recognition, and hardware interfacing.

Other Required Qualifications:

  • PhD in applied agricultural sciences, spatial statistics, data science, machine learning, computer vision, remote sensing or related field, or a Master’s degree in statistics, applied agronomy/ecology, computer science, machine learning and 5 years of experience.
  • Demonstrated research productivity through publications in relevant refereed journals, and an existing record of, or strong potential for, successful grant procurement.
  • Ability to conduct team-oriented research, exhibit exceptional leadership abilities, and demonstrate effective written and verbal communication skills.
  • Ability to migrate data streams into and out of the Esri GIS suite of products.
  • Familiarity with soil-landscape relationships, how soils influence plant growth, and soil survey databases available from NRCS.
  • Ability to develop, debug, and revise empirical and process-based models and enhance the spatial components of recommendation systems.
  • Ability to work independently and in a collaborative research environment is paramount.
  • Excellent organizational, interpersonal and data management skills.

Preferred Qualifications:

  • Excellent oral and written communication skills.
  • Working knowledge of user interface development systems used in software application development such as git, SQL, RStudio.
  • Working knowledge of Microsoft Windows, Microsoft Windows Server, and Linux.
    Working knowledge of cloud platforms (Azure, AWS or GEE).
  • Competence in server administration and basic cybersecurity of distributed systems.
  • Demonstrate experience building and applying imaging and analysis techniques to a wide range of problems in machine vision.
  • Demonstrated ability to deliver scientific results and communicate machine learning concepts utilized in a cross functional environment.

Required License(s) or Certification(s):

n/a

Valid NC Driver's License required:

No

Commercial Driver's License required:

No

Job Open Date:

11/10/2020

Anticipated Close Date:

Open Until Filled.

Special Instructions to Applicants:

Applicant Required Documents: Cover Letter, CV, and Contact Information for Three (3) Professional References.

Position Number:

00108234

Position Type:

EHRA Non-Faculty

Full Time Equivalent (FTE) (1.0 = 40 hours/week):

1.00

Appointment:

12 Month Recurring

Mandatory Designation - Adverse Weather:

Non Mandatory - Adverse Weather

Mandatory Designation - Emergency Events:

Mandatory - Medical Emergencies

Is this position partially or fully funded on ARRA stimulus monies?:

No

Department ID:

110901 - Crop and Soil Sciences

AA/EOE:

NC State University is an equal opportunity and affirmative action employer. All qualified applicants will receive consideration for employment without regard to race, color, national origin, religion, sex, gender identity, age, sexual orientation, genetic information, status as an individual with a disability, or status as a protected veteran.

If you have general questions about the application process, you may contact Human Resources at (919) 515-2135 or [email protected] Individuals with disabilities requiring disability-related accommodations in the application and interview process, please call 919-515-3148.

Final candidates are subject to criminal & sex offender background checks. Some vacancies also require credit or motor vehicle checks. If highest degree is from an institution outside of the U.S., final candidates are required to have their degree equivalency verified at www.wes.org or equivalent service. Degree(s) must be obtained prior to start date in order to meet qualifications and receive credit.

NC State University participates in E-Verify. Federal law requires all employers to verify the identity and employment eligibility of all persons hired to work in the United States.

 

 

 

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Job No:
Posted: 11/12/2020
Application Due: 1/11/2021
Work Type:
Salary: