Applied Research Lead, Reinforcement Learning
RunwaySalary not specified
Full Time
Junior (1 to 2 years)
Not SpecifiedCompensation
Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level
Full TimeJob Type
YesVisa
Industrial Automation, Artificial Intelligence, AI & Machine LearningIndustries
Requirements
Candidates should have a PhD in a relevant field such as machine learning, reinforcement learning, optimization, or causal inference. The role requires experience in developing novel algorithmic architectures and a strong understanding of applying AI to real-world problems, particularly in industrial applications. Preference will be given to candidates located in the UK, USA (California, Colorado, Connecticut, Georgia, Florida, Indiana, Maryland, Minnesota, Missouri, Nebraska, New York, North Carolina, Pennsylvania, South Carolina, Tennessee, Texas, Virginia, Washington), or Canada (Ontario, British Columbia, Alberta).
Responsibilities
Research Scientists will lead the development of novel algorithmic architectures for industrial control systems, utilizing reinforcement learning algorithms. They will design and implement prediction and decision algorithms for complex dynamic systems, and develop a benchmarking platform for performance evaluation. Responsibilities also include collaborating with other researchers, reporting findings internally and externally, and participating in collaborative research projects with external partners.
Skills
Reinforcement Learning
AI
Deep Learning
Sequential Decision Making
Industrial Automation
Data Analysis
Problem Solving
Collaboration
Communication
Phaidra
AI virtual plant operators for industrial efficiency
About Phaidra
Phaidra deploys artificial intelligence virtual plant operators to support operations teams in critical facilities. Their AI-powered control systems are designed to enhance stability, energy efficiency, and sustainability by learning and adapting over time. Unlike traditional static control systems that cannot adjust dynamically, Phaidra's technology continuously improves performance, addressing issues of degradation and lack of resiliency in industrial settings. The company has achieved notable success, such as delivering 40% energy savings at Google's data centers, and is expanding its solutions to various sectors, including pharmaceutical production and data centers. Phaidra's goal is to enhance operational efficiency and sustainability for its clients, generating revenue through the implementation and maintenance of its AI-driven systems.
Seattle, WashingtonHeadquarters
2019Year Founded
$41.3MTotal Funding
EARLY_VCCompany Stage
Industrial & Manufacturing, Energy, AI & Machine LearningIndustries
51-200Employees
Benefits
Health Insurance
Dental Insurance
Vision Insurance
Unlimited Paid Time Off
Parental Leave
Home Office Stipend
Company Equity
Risks
Increased competition from AI-driven industrial solutions providers could impact Phaidra's market share.
Rapid AI advancements may require Phaidra to continuously innovate, straining resources.
Potential data privacy concerns and regulatory scrutiny could challenge Phaidra's operations.
Differentiation
Phaidra offers AI solutions for mission-critical facilities, enhancing stability and efficiency.
The company combines AI expertise from Google-Deepmind with industry knowledge from Trane and Johnson Controls.
Phaidra's AI systems adapt and improve over time, unlike static, hard-coded control systems.
Upsides
Phaidra achieved 40% energy savings at Google's data centers, showcasing its effectiveness.
The rise of edge computing complements Phaidra's solutions for real-time analytics and efficiency.
Growing interest in AI-driven predictive maintenance aligns with Phaidra's focus on stability.
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