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(Kirchhoff's laws) as soft or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark
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, and efficiently across distributed stakeholders while preserving privacy and supporting future energy applications. Rather than simply implementing existing data space reference architectures, you will
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or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark datasets and an
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(Kirchhoff's laws) as soft or hard constraints, operational bounds (voltage limits, capacity, phase balance), and network topology through graph neural network architectures. You will build validated benchmark
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future energy applications. Rather than simply implementing existing data space reference architectures, you will develop novel methods, architectural patterns, and engineering approaches that enable