About the Role
ML Researcher Intern @ Plasmind (Part-Time, Remote)
Are you passionate about the intersection of AI and physical systems? This role offers the rare opportunity to contribute to foundational work in unconventional computing.
💻 What You’ll Do:
- Explore how physical systems can act as substrates in reservoir computing
- Conduct literature reviews on neuromorphic, analogue, and brain-inspired computing
- Apply reservoir computing frameworks to tasks such as time-series prediction or control
- Help prototype and evaluate hybrid ML algorithms tailored for physical reservoirs
- Work closely with our research scientist and hardware team on real-world experiments
🌱 Why Join Us?
At Plasmind, you’ll work on applied machine learning with a unique twist — bridging software and hardware in ways that push the limits of current AI thinking. This role is ideal for students or early-career researchers looking to strengthen their portfolio and experience in applied research, experimental ML, and cutting-edge computing.
Requirements
 What We’re Looking For:
Background in machine learning, computer science, physics, engineering, or a related field
Proficiency in Python and key ML libraries (e.g., PyTorch, scikit-learn, NumPy)
Strong interest in reservoir computing, nonlinear dynamics, or brain-inspired architectures
Analytical thinker with a deep curiosity about how physical systems can compute
Bonus: experience with reservoir computing models, hardware-in-the-loop systems, or ML for physical processes
About the Company
Plasmind Innovation is a pre-seed deep-tech startup designing analogue accelerator devices using low-temperature plasma and bio-inspired reservoir computing. We’re working at the edge of computation and physics, creating a new class of hardware that processes information through the spatio-temporal dynamics of physical systems — fast, adaptive, and energy-efficient.