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DOE Science, Technology and Policy Program– Office of Commercialization (OTC) Fellow

https://www.zintellect.com/Opportunity/Details/DOE-STP-OTC-2025

Application Deadline: July 29, 2025 @11:59PM Eastern Time

About this Opportunity

The mission of the Office of Technology Commercialization (OTC) is to expand the commercial and public impact of the research investments of the Department of Energy (DOE) and to focus on commercializing energy technologies that support the missions.

The fellowship is offering the opportunity to learn about the federal government and its role in commercializing energy technologies with the Office of Technology Commercialization (OTC) at the U.S. Department of Energy (DOE) in Washington, D.C. You will participate in projects and activities that support commercialization goals across the DOE energy and innovation portfolio by guiding strategy, conducting analysis, identifying opportunities to streamline and simplify processes, and designing & implementing funding programs in the following areas:

Commercialization Programs

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Research Opportunities

Computer Science, Artificial Inteligence

Work description

Development of an AI model in the context of non-invasive and ubiquitous pH/CO2 monitoring for use in marine environments, but with potential for application in other areas such as patient blood, with particular potential for application in intensive care settings.

The project is part of ongoing research by a multidisciplinary team that includes industry experts, optical sensors and instrumentation.

Academic Qualifications

Higher education in areas of Data Science, Artificial Intelligence, or related.

Minimum profile required

Degree in Data Science, Artificial Intelligence, and related areas. Immediate availability.

Preference factors

Master’s degree in Data Science, Artificial Intelligence, and related areas. Previous experience in similar projects.

Maintenance stipend: € 1040.98, according to the table of monthly maintenance stipend for FCT grants , paid via bank transfer. Grant holders may be awarded potential supplements, according

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Research Opportunities

Artificial Intelligence

Work description

The workplan begins with the analysis and preprocessing of historical data, focusing on identifying relevant variables, handling missing and noisy data, transforming textual variables, and storing the data on appropriate platforms. In the second phase, both global models (predicting overall product acceptability) and local models (predicting acceptability for specific consumer segments such as gender or age groups) will be developed using artificial intelligence techniques. Particular attention will be given to managing missing data and ensuring the application of transparent AI, with the goal of supporting decision-making in sensory studies.

The final phase involves applying advanced techniques such as transfer learning and ensemble methods to integrate historical and new data. Language models will be used to adapt data formats for consistency. The main expected outcome is a model that is tested and validated in a laboratory environment and prepared

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Research Opportunities

Artificial Intelligence

Work description

Develop a prototype AI conversational agent (AICA) based in LLM that answers user-posed questions in natural language about the impacts of the EU’s food system. It will utilise food linked life cycle assessment (LCA), footprint and other data developed within and outwith the project, collated into a central structured database.

Quantitative data will be complemented by information derived from research outputs and other assessments. Training of the AICA will take place via development of common questions inspired by the EU’s policy context and the guidance received via stakeholders.

Academic Qualifications

Quality academic background in Computer Science, Computer Engineering, Artificial intelligence, Data Science or equivalent.

Minimum profile required

In conditions that qualify for enrolment in a PhD program. Quality academic background in Computer Science, Computer Engineering, Artificial intelligence, Data Science or equivalent. Knowledge of Data Science,

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