Research Scientist in AI/ML for Dynamics and Control (Hybrid)
US-CT-EAST HARTFORD-RTRC L ~ 411 Silver Ln ~ RTRC L, United States
R
USD 86K-165K Senior-level Full Time
Tasks
- Build physics informed learning based control models
- Conduct Hardware-in-the-Loop validation
- Design control solutions for aerospace and defense applications
- Develop autonomy algorithms for simulation and hardware
- Develop intellectual property
- Develop machine learning for control
- Develop verification and validation methods for AI ML control
- Implement model predictive control
- Implement reinforcement learning for safety critical systems
- Model dynamical systems
- Publish research results in reports conferences and peer reviewed articles
- Train neural network controllers and estimators
- Write research proposals
Perks/Benefits
- 401k match
- Employee assistance program
- Employee scholar program
- Flexible work schedules
- Holidays
- Medical/Dental/Vision insurance
- Paid time off
- Parental leave
Skills/Tech-stack
Barrier functions | C# | C++ | CUDA | Control Theory | Control barrier functions | DSPACE | Data-Driven Modeling | Data-driven | Embedded Systems | FPGA programming | Hardware-in-the-loop | Kalman Filters | LQG | LQR | LabVIEW | MATLAB | Model-Predictive Control | Multivariable control | NI DAQ | Neural Networks | Optimization | Physics Informed Neural Networks | Physics-Based Modeling | Predictive control | PyTorch | Python | Real Time | Real-time control | Reinforcement Learning | Safety verification | Simulink | Speedgoat | State Estimation | System Modeling | TensorFlow | The Loop
Education
Roles
Engineer | Research Engineer | Research Scientist | Scientist
Regions
Countries
States
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