AI Research Scientist

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 reinforcement learning, deep learning, robotics and multi-agent reinforcement learning, our Research Scientists are at the forefront of groundbreaking research and applying them to real-world. Responsibilities Design, implement and evaluate models, agents and software prototypes of perceptual processing. 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. Work with external collaborators and maintain relationships with relevant research labs and key individuals as appropriate. Mentor and guide Research Engineers to apply research findings and developments to industrial domains. Key Qualifications Required: PhD in a technical field or equivalent practical experience, with demonstrated knowledge in one of the following: Reinforcement Learning Deep Learning Control Theory Multi-Agent RL Extensive research in the fields of (Model-Based, Model-Free, Safe) RL, Control Theory. Share our company values: curiosity, ownership, transparency & directness, outcome-based performance, and customer empathy. Preferred Skills & Experience A proven track record of publications. A real passion for AI and applying to industrial systems to improve resource efficiency.

Phaidra is building the future of industrial automation. Phaidra creates AI-powered control systems for the industrial sector, enabling industrial facilities to automatically learn and improve over time. Specifically: We use reinforcement learning algorithms to provide this intelligence, converting raw sensor data into high-value actions and decisions. We focus on industrial applications, which tend to be well-sensorized with measurable KPIs — perfect for reinforcement learning. We enable domain experts (our users) to configure the AI control systems (i.e. agents) without writing code. They define what they want their AI agents to do, and we do it for them.