EERC Research Assistant (Data Science and Artificial Intelligence) - Graduate Level

University of North Dakota•Grand Forks, ND

7d•Onsite

About The Position

This is a part-time, non-benefited position for a graduate student to support applied data science and artificial intelligence (AI) research at the Energy & Environmental Research Center (EERC). The student will work with EERC scientists, engineers, and data scientists on the Bakken 2.0 Cracking the Code (CtC) initiative, focusing on advancing the understanding and application of enhanced oil recovery (EOR) in the Bakken petroleum system. The role involves using computational and analytical approaches, including data curation, statistics, exploratory data analysis, machine learning, and advanced AI, to extract information from large, complex, multimodal Bakken datasets. A key focus will be on investigating how emerging AI capabilities, such as large language models and agentic AI, can enhance conventional data science methods for scientific discovery and decision support. The selected individual will collaborate with EERC researchers to develop practical, reproducible analytical workflows and translate complex datasets into actionable information.

Requirements

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
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.

Nice To Haves

Python scientific computing and data analysis, including tools such as NumPy, pandas, SciPy, scikit-learn, and visualization libraries.
Machine learning, deep learning, scientific machine learning, and model evaluation.
Large language models, generative AI, multimodal AI, and related emerging AI technologies.
AI agents, agentic workflows, or AI-assisted scientific research and knowledge discovery.
Analysis and integration of complex scientific data, including geospatial, time-series, engineering, and unstructured data.
SQL, databases, data engineering, or development of curated and reproducible research datasets.
High-performance, parallel, distributed, or cloud computing.
Energy, petroleum engineering, enhanced oil recovery, subsurface science, or related energy applications. Prior petroleum or geoscience experience is beneficial but not required.

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.