EERC Research Assistant; Data Science and Artificial Intelligence; Graduate Level

• $24.00 hourly, Non-Exempt (Eligible for overtime)
• Up to 20 hours per week
• 100% Remote Work Availability:
  No
• Hybrid Work Availability (requires some time on campus):
  No

This is a part-time, non-benefited position. Only UND students are eligible to be hired in this position.

We are looking for a graduate student to support applied data science and artificial intelligence (AI) research at the Energy & Environmental Research Center (EERC). The selected student will work with EERC scientists, engineers, and data scientists as part of the Bakken 2.0 Cracking the Code (CtC) initiative, which seeks to advance the understanding and application of enhanced oil recovery (EOR) in the Bakken petroleum system.

The work will focus on using multiple computational and analytical approaches, from data curation, statistics, and exploratory data analysis through machine learning and advanced AI, to extract information and insight from large, complex, multimodal Bakken datasets. These datasets may include well construction and completion information, production histories, reservoir and geological properties, geospatial data, engineering data, simulation results, technical literature, reports, figures, and other structured and unstructured information.

A particular emphasis will be placed on investigating how emerging AI capabilities, including large language models, multimodal AI, AI-assisted scientific workflows, and agentic AI, can complement conventional data science methods to accelerate scientific discovery and decision support.

The selected individual will work closely with EERC research scientists, engineers, and data scientists to develop practical, reproducible analytical workflows and translate complex datasets into information that supports research and engineering decisions.

Duties & Responsibilities:

• Assist in acquiring, organizing, curating, integrating, and quality-controlling structured and unstructured datasets relevant to Bakken research and enhanced oil recovery (EOR).
• Develop reproducible Python-based workflows for data processing, exploratory data analysis, visualization, statistical analysis, and machine learning.
• Apply statistical and machine-learning techniques to identify patterns, relationships, clusters, anomalies, and predictive features within complex scientific and engineering datasets.
• Integrate and analyze multimodal information, potentially including tabular, time-series, geospatial, technical-document, image, and other scientific data.
• Evaluate and develop applications of large language models, multimodal AI, and agentic AI for scientific data extraction, synthesis, analysis, reasoning, and knowledge discovery.
• Work with EERC scientists and engineers to evaluate AI- and data-driven results against conventional analytical methods, scientific understanding, and engineering principles.
• Document analytical methods, code, assumptions, data provenance, and results, and contribute to technical reports, presentations, visualizations, and other research products.

• Strong analytical and quantitative problem-solving skills.
• Ability to work with large, heterogeneous, and potentially incomplete scientific and engineering datasets.
• Ability to work independently while also contributing effectively to multidisciplinary research teams.
• Demonstrated curiosity and willingness to learn unfamiliar scientific and engineering subject matter.
• Ability to evaluate new computational and AI methods critically rather than treating AI-generated results as inherently reliable.
• Strong written and oral communication skills, including the ability to explain technical methods and results to researchers from different disciplines.
• Ability to develop organized, documented, and reproducible computational workflows.

• Enrolled in an M.S. or Ph.D. program in data science, computer science, electrical or computer engineering, applied mathematics, statistics, engineering, geoscience, or a related quantitative field.
• Experience programming in Python for scientific computing, data analysis, or machine learning.
• Experience working with common scientific data-analysis libraries and tools.
• Foundational knowledge of statistics, data analysis, and machine learning.
• Experience organizing, processing, and analyzing complex datasets.
• Excellent communication and interpersonal skills.
• Successful completion of a Criminal History Background Check

In compliance with federal…