Senior AI Research Scientist (Model-based RL)

• Design, implement, and evaluate model-based reinforcement learning agents — including planning-based controllers (MPC, MPPI) — and the software prototypes needed to deploy them on real industrial control systems.
• Develop learned dynamics and world models (learned surrogates) that generalize across systems, including the training pipelines — pretraining, curriculum learning, active/adversarial learning, and fine-tuning — needed to make them reliable for planning and control.
• Research and implement methods for e.g. safe RL, constrained control, scenario planning and Bayesian RL, to develop agents that satisfy safety constraints during deployment.
• Report and present research findings and developments including status and results clearly and efficiently both internally and externally, verbally and in writing.
• Participate in and organize ambitious collaborative research projects, and work with external collaborators and partners to translate research into production outcomes.
• Mentor and guide Research Engineers to apply research findings and developments to industrial domains.
• Independently defines new research directions
• Translates research into practical outcomes
• Owns the development and rollout for an entire research area or large project

Who You Are

Research Scientists at Phaidra lead our efforts in developing novel algorithmic architecture towards the end goal of bringing intelligent control systems to industrial sector.

Having pioneered research in the world's leading academic and industrial labs as PhDs, post-docs or professors, Research Scientists join Phaidra to work collaboratively within and across research fields.

Drawing on expertise from a variety of disciplines including model-based reinforcement learning, planning and optimal control, deep learning, world models, and safe reinforcement learning, our Research Scientists are at the forefront of groundbreaking research and applying it to real-world industrial control systems.

Key Qualifications

• PhD in a technical field or equivalent practical experience, with a strong background in model-based reinforcement learning and demonstrated knowledge in one or more of the following:
• Planning algorithms
• World models / learned dynamics surrogates
• Reinforcement Learning and Deep Learning
• Control Theory
• Safe / constrained RL
• Either:
• 2+ years of research experience in academia or industry after PhD graduation.
• 5+ years of research experience in academia or industry after Master’s graduation.