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understanding of the interaction between materials, environment, and loading. This PhD research will focus on developing mechanistic and data-driven tools to assess and predict the long-term performance
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verification tools. AMS test diagnostics and fail-log analysis. Your Role as a PhD Candidate You will: Analyze and enhance current automotive IC test flows. Develop new or improved fault models using NXP’s in
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., VAEs, GANs, diffusion models) or graph/equivariant neural networks for materials (e.g., CGCNN, SchNet, NequIP), and related tooling (e.g., PyTorch Geometric, pymatgen, ASE) is beneficial but not required
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experience and productivity of software engineers at the scale of Meta's tens of thousands of software engineers. Across several tracks, we will develop tools that rethink the future of software engineering
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experience and productivity of software engineers at the scale of Meta's tens of thousands of software engineers. Across several tracks, we will develop tools that rethink the future of software engineering
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of clearroom tools that will focus on fabrication of both widebandgap and 2D materials from the Scalable Atomic Processing Line (SAP-NL). Job requirements Master Degree in Electrical Engineering or
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AI methods and tools that capture the complexity of the port call process and handle uncertainties caused by the energy transitions. In collaboration with the other researchers, this will include
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and sensor characterization. On the horizon is a new suite of clearroom tools that will focus on fabrication of both widebandgap and 2D materials from the Scalable Atomic Processing Line (SAP-NL). Job
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efficient port calls. In the PortCall.Zero project, you will develop novel AI methods and tools that capture the complexity of the port call process and handle uncertainties caused by the energy transitions
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creation that controls clogging patterns Developing predictive digital rock physics and permeability evolution models from µCT data using machine learning and computational tools (PuMA/CHFEM/MOOSE) validated