Manifold Research Group tackles ambitious, high-impact research problems that traditional institutions overlook—those too engineering-intensive for academia and too exploratory for industry. Inspired by coordinated research models like ARPAs and FROs, we coordinate focused, cross-functional teams and a large asynchronous research contributor pool to systematically pursue and deliver paradigm-shifting science and technology.
Navigation in Crowded Orbital Environments
Navigation in Crowded Orbital Environments is a research project focused on developing scalable autonomy for spacecraft operating amid debris, traffic, and other agents in dense orbital regimes. The project explores machine learning methods for mapping complex initial conditions directly to navigation strategies, with the goal of enabling real-time, collision-free maneuvering in highly congested space environments.
A central direction in this effort is the use of biological structure as an inductive bias for learning. In particular, we are interested in leveraging the connectome of the fruit fly as a structural prior for neural architectures, investigating whether brain-inspired organization can improve generalization, efficiency, and adaptability in orbital navigation tasks. This work sits at the intersection of machine learning, neuroscience-inspired computation, and autonomous space systems.
The Role
OS Team members form the core of Manifold Research Group. As an OS Research Fellow, you will contribute to the brain-inspired modeling layer of the Navigation in Crowded Orbital Environments project.
In this role, you will be responsible for:
- Designing and implementing neural architectures that use biological connectivity priors, including connectome-inspired structures
- Translating fruit fly connectome organization into usable machine learning models and experimental frameworks
- Running experiments to evaluate whether brain-inspired inductive biases improve navigation performance, generalization, or efficiency
- Supporting integration of these models into orbital navigation and multi-agent autonomy settings
- Contributing to research publications, experimental infrastructure, and technical direction for the project
Qualifications
Outstanding research emerges from individuals who can bridge disciplines and execute technically across both theory and systems. For this role, we are looking for:
- Strong software engineering and machine learning skills, particularly in Python
- Experience with neural network architecture design and deep learning frameworks such as PyTorch or JAX
- Background in computational neuroscience, connectomics, brain emulation, or biologically inspired machine learning
- Ability to translate scientific structure or biological priors into implementable model architectures
- Comfort working in ambiguous research settings with evolving hypotheses and experimental directions
Expectations
There are a few key expectations and clarifications regarding the OS Research Team:
- Contribute approximately 10 hours per week to ensure meaningful progress and deep engagement with our projects. Flexibility around life commitments is understood; clear, proactive communication helps us support each other.
- Our working language is English, and strong proficiency is required to clearly communicate technical concepts without confusion or misunderstanding.
- This is a volunteer effort; none of us receive compensation of any kind, including monetary payment, academic credit, or other formal incentives. Our commitment is driven entirely by shared passion for impactful research.
More information on OS Research Team expectations is available here.
We look forward to seeing your application, and hopefully working together soon!