Teams at Aalto University

Conduct research in trustworthy machine learning, focusing on model and pipeline provenance, integrity, adversarial robustness, privacy, fairness, AI/MLBOM, and proof-carrying machine learning. Publish research, present findings, collaborate internationally, and contribute to teaching. Candidates need strong Python and PyTorch skills, applied mathematics expertise, shared-codebase experience, and a completed Master's degree before the contract begins.

Develop fundamental theory and practical algorithms for continual reinforcement learning in non-ergodic, changing real-world systems. Responsibilities include trajectory-centric optimization, change detection, adaptive policies with safety guarantees, integration into state-of-the-art reinforcement learning algorithms, and validation through simulations and robot hardware experiments. Researchers will work collaboratively within Aalto University’s Cyber-physical Systems Group and pursue doctoral studies in electrical engineering.

Build hardware-aware AI behavioral models for analog and RF circuits using Python, validate them against simulations and measurements, and perform hardware-algorithm co-design for energy-efficient 6G transmitters. The role includes publishing research, completing a doctoral thesis, teaching, and collaborating internationally. Candidates need a relevant Master's degree, Python-based machine learning experience with PyTorch or similar tools, and strong backgrounds in signals, systems, and analog circuit theory.