Data-Driven Model Developer

3 days ago

Gandhinagar, India beBeeMachine Full time

Job Description:We are seeking a skilled professional to develop and deploy Graph Neural Network (GNN) based surrogate models that approximate complex physics simulations for oil & gas pipeline and well networks.This is a hands-on role for someone who can build high-fidelity neural network models that replace computationally expensive reservoir and network simulators (Nexus, Prosper).The ideal candidate will design and implement Neural Network architectures to model flow dynamics in interconnected pipeline networks, building surrogate models that accurately predict pressure distributions, flow rates, and network behavior under varying operational scenarios.Create data pipelines to extract network topology and simulation results from physics-based models (Nexus/Prosper)Transform them into graph representationsDevelop training frameworks that incorporate physics constraints into neural network loss functionsRequired Skills and Qualifications:The ideal candidate has strong expertise in Graph Neural Networks (GCN, GraphSAGE, Message Passing Networks), deep understanding of deep learning frameworks (PyTorch Geometric, DGL, or TensorFlow GNN), experience building surrogate models or physics-informed neural networks (PINNs) for engineering applications, proficiency in Python and scientific computing libraries (NumPy, SciPy, Pandas), and demonstrated ability to work with complex data structures (graphs, time-series, spatial data).Benefits:This role offers the opportunity to work on cutting-edge projects, collaborate with experienced engineers, and contribute to the development of innovative solutions for the energy industry.Others:Please note that this job description is not exhaustive and may be subject to change based on business needs. We are an equal opportunities employer and welcome applications from diverse candidates.

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