Verified current PhD Opportunity

Graduate/PhD Research Intern, Machine Learning

The Role

PhD Opportunity Full source details
Constellationspace Seattle Source published Sep 20, 2026 Verified 5 hours ago
✓ 100% verification score · Source: Constellationspace (ashby) · Always confirm final requirements on the original source.
Complete source information imported The available role or programme description, requirements, benefits and source facts were imported from the public official endpoint and formatted for reading.

Overview

The Role

Complete research opportunity details

The Role Constellation is seeking an ambitious Graduate or PhD Research Intern to join our Data Science / AI team. You will research and develop cutting-edge machine learning models to solve complex aerospace challenges, from predictive maintenance to autonomous orbital navigation. Responsibilities

  • Conduct independent research to design and train novel machine learning architectures.
  • Analyze massive datasets derived from flight telemetry and satellite sensors.
  • Prototyping and testing algorithms in simulated aerospace environments.
  • Publish internal papers and present findings to the core engineering team. Requirements
  • Currently pursuing a Master's or Ph.D. in Computer Science, Aerospace Engineering, Mathematics, or a related field.
  • Strong theoretical understanding of deep learning, computer vision, or reinforcement learning.
  • Proficiency in Python and standard ML frameworks (PyTorch, TensorFlow, or JAX).
  • Ability to work on-site in Seattle for the duration of the internship.

Tips for this phd opportunity

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  1. Match your research background to the project, laboratory, supervisor and required methods rather than applying only from the title.
  2. Prepare a focused academic CV, transcripts, publications and research statement or proposal when requested by the official call.
  3. Confirm funding duration, stipend/salary, tuition coverage, start date and eligibility for international applicants.
  4. Contact a supervisor before applying only when the official instructions recommend or require it.

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