We are seeking an Energy AI Research Engineer to develop advanced data-driven and physics-informed solutions for renewable energy systems, power grid optimization, and market operations. The role involves designing, training, and deploying models that integrate machine learning, control theory, and power system dynamics to address challenges in forecasting, grid stability, distributed energy resource (DER) integration, and demand-side management.

Required Skills

Strong background in machine learning (supervised, reinforcement, causal inference, probabilistic modeling).
Expertise in power systems, renewable energy integration, and control theory.
Experience with physics-informed ML and hybrid modeling approaches.
Proficiency in optimization methods (linear, nonlinear, stochastic, robust).
Knowledge of signal processing, computer vision, and satellite image analysis.
Proficiency in Python, MATLAB, or Julia; experience with ML frameworks (PyTorch, TensorFlow).
Familiarity with power system simulators (MATPOWER, OpenDSS, DIgSILENT PowerFactory) and energy market tools.
Cloud computing and high-performance computing (HPC) experience for large-scale simulations.
Distributed Energy Resources (DER), solar PV, wind, BESS, EV charging systems.
Grid stability and control of inverters, microgrids, and demand response systems.
Energy economics and market bidding strategies under uncertainty.
Prior academic research, writing, and publishing experience.

To apply:

Share your prior research works, such as Google Scholar or ORCID ID in resume

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Researcher - Renewable Energy Systems