Graduate Student Intern – Data Science & Remote Sensing
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About this role
About Us
At KBR, we deliver science, technology, and engineering solutions that help our customers accomplish their most critical missions. Join us at the Earth Resources Observation and Science (EROS) Center and help shape the future of Earth science data systems. As part of our mission-driven team, you’ll contribute to projects that make a global impact, supporting research, innovation, and technology that empower scientists and decision-makers worldwide.
Overview
We are seeking a highly motivated graduate student with a strong background in data science and deep learning to contribute to projects at the intersection of satellite remote sensing and AI-driven analytics. The intern will collaborate with scientists at EROS who provide domain expertise in remote sensing physics, while the intern focuses on advanced machine learning techniques for geospatial applications.
Must have Three years of continuous residency in the US
Key Responsibilities
- Develop and implement deep learning models for remote sensing applications, with emphasis on:
- Designing custom Convolutional Neural Networks (CNNs) for domain adaptation (e.g., transferring Landsat standards to UAS imagery).
- Writing custom loss functions and leveraging automatic differentiation in frameworks like PyTorch or TensorFlow.
- Work with geospatial datasets and apply preprocessing techniques for radiometric calibration.
- Collaborate with domain experts to integrate physics-based constraints into AI models.
- Understands converting raw digital numbers (DN) to Top-of-Atmosphere (TOA) reflectance and at-sensor radiance.
Required Skills & Knowledge
- Deep Learning & AI Architectures:
- Proficiency in PyTorch or TensorFlow.
- Experience with CNN design, optimization methods, and domain adaptation.
- Programming:
- Strong coding skills in Python (and optionally C++) for scientific computing.
- Remote Sensing Fundamentals:
- Familiarity with BRDF concepts, multispectral/hyperspectral data, and geospatial formats (e.g., GDAL).
- Mathematical Foundations:
- Solid understanding of linear algebra, multivariable calculus, and optimization techniques.
Recommended Academic Background
Candidates should have completed coursework in the following areas:
- Artificial Intelligence: Deep Learning, Computer Vision, Physics-Informed Neural Networks, Optimization Methods.
- Remote Sensing: Multispectral and Hyperspectral Remote Sensing.
- Geospatial Science: Geospatial data formats and tools (e.g., GDAL).
- Mathematics/Physics: Linear Algebra, Multivariable Calculus, Electromagnetic Theory.
- Computer Science: Python or C++ for scientific computing.
Preferred Qualifications
- Prior experience with geospatial data analysis or remote sensing projects.
- Ability to work independently and collaborate effectively in a multidisciplinary team.
Special Requirements
Three years of continuous residency in the US for issuance of a Government Security credential. The candidate must be able to obtain and maintain a national agency check and background investigation after hiring to obtain a badge for government facility access and user account. Experience and/or education in lieu of these qualifications will be reviewed for applicability.
Experience and/or Education in lieu of these qualifications will be reviewed for applicability to meet these requirements.