Soil Spectroscopy and Chemometrics Co-op

Syngenta Group•Des Moines, IA

9d•Onsite

About The Position

Syngenta's Seeds Research Portfolio and Project Management Team is seeking a Soil Spectroscopy and Chemometrics Co-op in Des Moines, IA. This internship role will work with our soil scientists and data scientists on projects that leverage Fourier-transform infrared (FTIR) spectroscopy and advanced chemometric and machine learning methods to rapidly characterize soil properties at scale. As a co-op, you will work at the intersection of soil science, spectroscopy, and data science, developing and validating calibration models, exploring novel modeling approaches, and helping establish workflows that connect spectral data to actionable agronomic insights. As part of Syngenta's Emerging Talent Program, you will gain hands-on experience, exposure to day-to-day business operations, and opportunities to apply your academic knowledge to real-world challenges while developing your professional skills. Duration: May 2027 – August 2027

Requirements

Student must be currently pursuing a Ph.D. in Soil Science, Environmental Science, Chemometrics, Data Science, Agricultural Engineering, Analytical Chemistry, or a related field.
Advanced Ph.D. students with existing spectroscopy or chemometrics research experience are strongly preferred.
Must be a current student continuing their education upon completion of the co-op term (May 2027 through August 2027).
All applicants must be eligible to work in the U.S.
Demonstrated experience with vibrational spectroscopy data (MIR, NIR, and/or Vis-NIR), such as spectral preprocessing and multivariate calibration.
Proficiency in one or more programming languages used in chemometrics and data science (R, Python).
Experience accessing and working with soil spectral libraries (e.g., USDA KSSL, OSSL).
Experience with chemometric methods (particularly Partial Least Squares Regression) and familiarity with machine learning approaches for spectral data (e.g., Random Forest, CNNs, transformer-based models).
Understanding of soil science fundamentals, including soil properties, analytical methods, and the physical basis of spectral absorption features in soils.
Scientific curiosity and passion for creative problem-solving.
Ability to communicate complex ideas and results in a clear and actionable manner.
Comfortable working independently and as part of a team.

Nice To Haves

Familiarity with calibration transfer methods and multi-instrument workflows is a plus.

Responsibilities

Build, evaluate, and refine calibration models that predict soil physical and chemical properties (e.g., organic carbon, texture, pH, CEC, nutrients) from infrared spectra.
Apply spectral preprocessing techniques (e.g., Savitzky-Golay derivatives, scatter correction, baseline correction) and evaluate their impact on model performance.
Work with large soil spectral libraries, comprising instrument standardization and calibration transfer challenges.
Develop and compare chemometric (PLSR) and machine learning (Random Forest, CNN, etc.) approaches for soil property prediction.
Collaborate closely with a multidisciplinary team of soil scientists, agronomists, and data scientists.
Communicate methods and results clearly to both technical and non-technical stakeholders.
The intern will operate in accordance with Syngenta Health, Safety, & Environmental (HSE) standards.