Project Description
About
At Vinterstellar, we design tools and strategies for the future of satellite operations. We’re now offering a unique Master Thesis project that explores how Artificial Intelligence (AI) and Machine Learning (ML) can revolutionize Electrical Orbit Raising (EOR) trajectory optimization — making the process faster, smarter, and more robust.
Thesis Objective
The goal is to develop a machine learning pipeline that predicts optimal weighting factors for EOR trajectory generation. Instead of relying solely on slow iterative optimization methods, you will investigate how AI can provide either high-quality initial guesses or directly predict optimal weights — reducing computation time and improving solution robustness.
What You’ll Gain
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Hands-on experience in applied AI/ML for space systems
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Insights into orbital mechanics, trajectory design, and optimization
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Skills in combining numerical optimization with data-driven methods
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A chance to contribute to the development of Vinterstellar’s One-Line Orbit Calculator
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Direct collaboration with engineers and researchers in the Swedish space ecosystem
What you will work on
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Dataset Generation: Automate the creation of training datasets from existing optimization runs.
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Feature Engineering: Identify and preprocess key mission descriptors (orbital parameters, spacecraft characteristics, etc.).
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Model Training & Evaluation: Explore regression models, ensemble methods, and neural networks to predict optimal weight vectors.
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Pipeline Development: Build a modular system integrating dataset generation, training, and prediction.
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Benchmarking: Compare AI-assisted optimization with traditional methods in terms of speed, robustness, and solution quality.
Location & Collaboration
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Work closely with the Vinterstellar team
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Duration: ~5 months (thesis project)
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Flexible setup (remote or hybrid with touchpoints in Sweden)
How to Apply
Send your CV, transcript, and a short motivation letter to: career@vinterstellar.se
Applications reviewed continuously.
Who Should Apply?
We’re looking for motivated master’s students in:
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Aerospace Engineering (with focus on astrodynamics or mission analysis)
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Computer Science / Data Science (with interest in space applications)
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Engineering Physics, Applied Mathematics, or similar fields
An ideal candidate has strong programming skills (Python, MATLAB), an interest in ML, and curiosity about applying AI to real-world space engineering problems.