Principal Applied AI Research Engineer or Geoscientist

• $124200 annual dependent on relevant experience Exempt
• 40 hours per week
• 100% Remote Work Availability: Yes
• Hybrid Work Availability (requires some time on campus): Yes

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 Were 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 .

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 worlds energy and environmental challenges.

• 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.

• Bachelors 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

In compliance with federal law all persons hired will be required to verify identityand eligibility to work in the US and to complete the required employment eligibility verification form upon hire.

• 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.

To ensure full consideration applications must be received by October 15 2026.