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Neara is seeking a Principal Research Scientist - Risk to join its internal research group and shape methods for resilient, reliable electricity grids. You will define the research agenda, develop rigorous models, and translate findings into actionable tools for utilities managing large-scale capital expenditure.
The role emphasizes probabilistic modeling, uncertainty quantification, and decision analytics, applied to asset failure, vegetation risk, and wildfires.
Imagine having the power to stress-test an entire power grid against a hurricane or thunderstorm before the clouds even gather. That is the reality we are creating at Neara.
We use advanced machine learning to create engineering-grade, physics enabled digital twins of electricity grids across four continents, this helps asset owners understand their biggest challenges and bring the most viable solutions to life across millions of kilometres of infrastructure.
By simulating extreme weather and structural stress at a network-wide scale, we empower the world’s largest utilities to pinpoint risks, optimise investments and build a more resilient global energy future.
Our team is a collection of brilliant minds who are fanatical about making a tangible difference in the real world, utilising AI and machine learning to accelerate everything from data classification to complex scenario analysis. We have built a special culture where innovation thrives because everyone owns the mission and we need smart, creative people to help us scale this impact to every corner of the globe.
We are seeking an experienced research scientist to contribute to Neara’s internal research group in topics of system resilience, reliability, and policy. In this role, you will define a research agenda, develop rigorous statistical and decision models, and translate findings into tools and recommendations for utilities managing billions of dollars in capital expenditure.
Neara provides network-scale structural, loading, and contextual data across complete overhead electrical systems. As a Principal Research Scientist - Risk, you will use this information to build generalisable models of asset failure and determine the value of additional data, inspections, and interventions.