Principal Applied AI Research Engineer or Geoscientist

University of North Dakota•Grand Forks, ND

19d•$124,200•Hybrid

About The Position

Are you ready to apply artificial intelligence and advanced analytics to solve complex challenges in subsurface energy and oil and gas development? Do you have the technical expertise to turn large geoscience and engineering datasets into actionable insights for reservoir characterization, production, EOR, and subsurface storage? We’re looking for a Principal Reservoir Engineer/Geoscientist with applied AI/machine learning experience to join our dynamic team at the Energy & Environmental Research Center (EERC)! Join the EERC, an international leader in energy and environmental research and a nonprofit branch of the University of North Dakota (UND), which gives our employees access to unbeatable benefits and tuition waivers. Learn more about the EERC at undeerc.org. Be a part of cutting-edge research, engage with professionals who share your passion, and create work you can be proud of. As a Principal Applied AI Research Engineer or Geoscientist, you will contribute to the success of the organization by performing one or more of the following: Evaluate and interpret geoscience and engineering data using advanced analytics and machine learning techniques to better inform unconventional and conventional oil and gas development, enhanced oil recovery (EOR), and subsurface gas or liquids (i.e., hydrocarbon, CO2, or hydrogen) storage. Integrate machine learning, visualization, and artificial intelligence into existing workflows for upstream oil and gas applications, CO2 storage, geophysical data processing and interpretation, or other subsurface modeling and simulation workflows. Develop, validate, and deploy data-driven models for reservoir characterization, production forecasting, anomaly detection, EOR screening, and subsurface storage performance evaluation. Apply supervised and unsupervised machine learning, predictive analytics, uncertainty quantification, and statistical modeling to large subsurface, production, completion, and operational datasets. Translate AI and machine learning results into technically defensible reservoir engineering interpretations, recommendations, reports, proposals, publications, and client-facing presentations. Build awareness within the EERC of AI/data mining/advanced analytics applications to elevate the knowledge of these techniques across the EERC geoscience and engineering teams. Constructively work with other groups at the EERC to assist with and prepare proposals to potential clients and to manage project tasks, budgets, and deliverables. Present technical findings through reports, publications, conference presentations, and client briefings. Occasional travel is required. The EERC is recognized as a global leader in developing clean, efficient energy solutions. With a dedicated team, we collaboratively develop innovative technologies to power the world efficiently while safeguarding our air, water, and soil. Trusted by over 1300 clients in 53 countries, our mission is to provide practical, pioneering solutions to the world’s energy and environmental challenges.

Requirements

Excellent communication, teamwork, interpersonal, and customer service skills.
Effective project, task, budget, deliverable, and personnel management in the area of expertise.
Recognized expertise in a relevant technical field and a commitment to learning and applying new technologies.
Demonstrated ability to evaluate the quality, limitations, uncertainty, and applicability of AI and machine learning outputs for subsurface characterization, geomodeling, reservoir engineering, or geophysical interpretation.
Strong data literacy, including identifying data quality issues, documenting assumptions, and communicating model uncertainty to technical and nontechnical audiences.
Ability to translate AI and machine learning results into technically defensible engineering and geoscience interpretations and recommendations.
Excellence in mentoring, advising, and/or supervising staff.
Strong leadership and constructive collaboration across multidisciplinary teams.
Bachelor’s degree in petroleum or geological engineering, geology, geophysics, or related engineering, science, or geoscience field.
5+ years of relevant project experience in successfully launching, planning, and executing data science projects, preferably in the domains of oil and gas development and production, petroleum geology, subsurface characterization, and/or subsurface gas or liquids storage.
Demonstrated experience applying artificial intelligence, machine learning, advanced analytics, or statistical modeling to engineering, geoscience, production, reservoir, or other energy-related datasets.
Working knowledge of machine learning concepts such as model training, validation, feature engineering, bias/variance, overfitting, model interpretability, and uncertainty communication.
Experience using programming, scripting, or analytics tools such as Python, R, MATLAB, SQL, Jupyter notebooks, or similar platforms to analyze technical datasets.
Experience leading technical projects, proposals, reports, and/or publications and working directly with industry, government, or research clients.
Cover letter and resume or curriculum vitae.
Successful completion of a Criminal History Background Check

Nice To Haves

Advanced degree in petroleum or geological engineering, geology, geophysics, or related engineering, science, or geoscience field.
Five or more years of experience with conventional and/or unconventional reservoir simulation, oil and gas reservoir resource assessment, EOR project evaluation or implementation, subsurface gas or liquids storage, and/or geothermal project implementation.
Coding proficiency in Python, R, Matlab, or comparable languages.
Experience with machine learning libraries or workflows such as scikit-learn, TensorFlow, PyTorch, XGBoost, MATLAB toolboxes, MLflow, or comparable tools.
Experience developing AI-enabled workflows for reservoir simulation support, production forecasting, decline analysis, pressure transient interpretation, well spacing evaluation, completion optimization, or field development planning.
Experience integrating physics-based reservoir engineering methods with data-driven models, including hybrid modeling, proxy modeling, reduced-order modeling, or digital twin concepts.
Experience with data management practices for technical AI projects, including data cleaning, feature creation, reproducible workflows, version control, metadata documentation, and model documentation.
Demonstrated technical writing ability, such as author or coauthor of published peer-reviewed articles or technical papers.
Experience writing successful research proposals for competitive funding.

Responsibilities

Evaluate and interpret geoscience and engineering data using advanced analytics and machine learning techniques to better inform unconventional and conventional oil and gas development, enhanced oil recovery (EOR), and subsurface gas or liquids (i.e., hydrocarbon, CO2, or hydrogen) storage.
Integrate machine learning, visualization, and artificial intelligence into existing workflows for upstream oil and gas applications, CO2 storage, geophysical data processing and interpretation, or other subsurface modeling and simulation workflows.
Develop, validate, and deploy data-driven models for reservoir characterization, production forecasting, anomaly detection, EOR screening, and subsurface storage performance evaluation.
Apply supervised and unsupervised machine learning, predictive analytics, uncertainty quantification, and statistical modeling to large subsurface, production, completion, and operational datasets.
Translate AI and machine learning results into technically defensible reservoir engineering interpretations, recommendations, reports, proposals, publications, and client-facing presentations.
Build awareness within the EERC of AI/data mining/advanced analytics applications to elevate the knowledge of these techniques across the EERC geoscience and engineering teams.
Constructively work with other groups at the EERC to assist with and prepare proposals to potential clients and to manage project tasks, budgets, and deliverables.
Present technical findings through reports, publications, conference presentations, and client briefings.