EM University Programs AI and Machine Learning Intern

Savannah River National Laboratory is seeking a highly motivated and self-starting AI and machine learning student intern researcher to join our team in developing cutting edge AI workflows for knowledge management, information retrieval, and monitoring complex environmental and physical systems. The successful candidate will have strong software development experience in Python and broad knowledge of AI and machine learning techniques and an ability to apply them to broad problem sets in the areas of bioengineering, environmental monitoring, and national security related domains.

• Develop and maintain AI and machine learning software pipelines for monitoring complex and dynamic physical systems
• Collaborate with team members to develop and maintain research tools and software applications
• Write and maintain technical documentation for research tools and software applications
• Participate in code reviews and contribute to the improvement of the overall codebase
• Stay up-to-date with the latest developments in AI and machine learning models

Typical Tools and Technologies:

• Python libraries: NumPy, pandas, SciKit-Learn, Pytorch, TensorFlow
• Data visualization tools: Plotly/Dash, Matplotlib, Seaborn
• Machine learning frameworks: SciKit-Learn, Pytorch, TensorFlow
• Others as the technology stack changes

Minimum Qualifications:

• Must be a U.S. Citizen
• Pursuing a degree in computer or physical sciences
• Strong experience in Python programming, including experience with AI and machine learning libraries (e.g. Pytorch, TensorFlow, scikit-learn)
• Demonstrated ability to apply AI/ML concepts
• Excellent communication and teamwork skills
• Ability to write and document technical work
• Self-motivated and able to work independently
• Familiarity with data engineering and curation principles and practices

• Experience with data visualization tools

Preferred Qualifications:

• Experience in software development, preferably in a research environment
• Experience with machine learning primitives and ability to choose the right approach for a given problem (e.g. decision trees, random forests, deep learning)
• Experience with natural language processing (NLP) techniques and libraries (e.g. NLTK, spaCy), as well as knowledge management principles