DESCRIPTION We are seeking a motivated graduate student in a STEM discipline enrolled in a PhD program who is interested in pursuing their passion by working side-by-side with world experts through an internship at the U.S. Department of Energy’s (DOE) National Energy Technology Laboratory (NETL). As an intern, you will work as part of DOE’s leading engineering Research and Development laboratory team supporting NETL to address challenges in decarbonization, renewable energy, and carbon reduction. Specifically, you will work with a team on a novel Laboratory Directed Research and Development project investigating the potential to enhance geothermal systems with the greenhouse gas carbon dioxide to meet dual objectives of renewable energy generation and greenhouse gas storage, supporting our national net-zero carbon mission. In this work, you will develop and apply machine leaning techniques to aggregate and analyze available knowledge and datasets to evaluate carbon dioxide injection in geothermal systems for cost effective and safe exploration and operation. In this role, you will engage across various expertise domains including geologists, petroleum engineers, and data and artificial intelligence/machine learning scientists and engineers to execute a high impact, multidisciplinary project with a diverse team. What this opportunity with Leidos supporting NETL uniquely provides you: Working on applied, cutting-edge projects with national impact while being mentored by the nation’s leading energy scientists and engineers. Real-world experience supplemented with technical development and discussions that provide unique insight into the broad range of mission critical engineering and scientific disciplines NETL leverages to support national energy research and policy development. Access to expert regulatory and industrial experience and knowledge on how data is collected, managed, and analyzed to provide credible risk evaluation for decision making. Primary investigations of applications for carbon dioxide storage and renewable energy development. Primary Responsibilities Collect and evaluate with domain experts for quality control various datasets on geology, geotechnology, geomechanics, geothermal energy, and subsurface technology models and simulations. Evaluate and process the data for feature generation and dimension reduction. Develop and conduct machine learning and deep learning workflows to integrate datasets from multi-physics simulation analyses through applying skills in computer programming/coding. Run existing machine learning/deep learning modeling codes and develop new models for evaluating the potential to use carbon dioxide to enhance existing geothermal production and infrastructure. Work collaboratively with mentors and affiliated team members to deliver high-impact results suitable for transferable application/deployment for other fields and potential publication in a leading technical journal or presentation at a conference. Collaborate on discussions, presentations, reports as needed. Basic Qualifications Currently attending and enrolled full time in an accredited science, engineering, or math Ph.D. program and completed most of the course work with minimum cumulative GPA of 3.0/4.0. The planned graduation/defense is at least 2 years away. Proficiency with machine learning analysis and deep learning application development for prediction and forecasting using multiple datasets. Proficiency with modern computer coding languages such as PYTHON, MPI, Java. Experience with modular computer software development practices. Experience presenting and discussing scientific research and results. Availability of approximately 10 to 20 hours per week for 21 month appointment. Preferred Qualifications Experience developing and applying machine learning methods for subsurface or energy production enhancement and/or energy infrastructure analysis and evaluation. PAY RANGE: $53,300.00 – $82,000.00 – $110,700.00 The Leidos pay range for this job level is a general guideline only and not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.