Responsibilities

• Interns put inquiry and theory into practice. Alongside fellow doctoral candidates and some of the world’s best researchers, interns learn, collaborate, and network for life. Interns not only advance their own careers, but they also contribute to exciting research and development strides. Interns are paired with mentors and expected to collaborate with other interns and researchers, present findings, and contribute to the vibrant life of the community.

Responsibilities include:

• Review existing literature and identify gaps in the state-of-the-art and formulate research questions
• Design experiments that apply cutting-edge research in machine intelligence and machine learning. Implement prototypes of scalable systems in AI applications.
• Collaborate closely with team members and potential external collaborators on developing systems from prototyping to production level.
• Under instruction from others, research new tools, technologies, and methods being used in the research community and contribute your knowledge around a specialized tool/method to support planning for research projects.

Qualifications

Required Qualifications (RQs)

• Must be currently enrolled in a PhD program in Computer Science or a related STEM field.
• Must have at least one additional quarter/semester of school remaining following the completion of the internship
• Must have at least 2 years of experience with modeling in Python and at least 2 years of experience with deep learning models.
• Must be fluent in English (verbal and written)

Preferred Qualifications (PQs)

• Experience with one or more general purpose programming languages including: C/C++, Java, MATLAB or Python, ideally languages such as pyTorch, Tensorflow and other deep learning toolkits
• Experience (classroom or work related) in one or more areas of computer science, such as Natural Language Understanding, Neural Networks, Computer Vision, Machine Learning, Deep Learning, Algorithmic Foundations of Optimization, Data Science, Privacy, etc.
• Experience with geospatial machine learning, foundation models, agentic AI, or low-resource language models (development and evaluation), applied to practical challenges in agriculture, biodiversity, public health, or disaster response, is a plus.