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descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers). Preferred Qualifications: Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other
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such as federated learning. Provenance and Reproducibility Frameworks: Build systems that enable detailed provenance tracking, schema validation, and auditable workflows to ensure trustworthy and
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independent, objective, and credible research analysis to inform decision-making. To gain insights on manufacturing supply chains, the team conducts techno-economics, value chain, trade, patent, and global
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, objective, and publicly available geothermal well life cycle assessment analysis. This project will conduct research into alternative geothermal well designs that can improve the life cycle value
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industrial heat pumps). The role also involves research and analysis of technologies and practices for increasing the energy efficiency of the industrial sectors. The primary objective is to deliver the direct
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field completed within the last five years. Good track record in scattering theory, quantum many-body theory, thermodynamics, statistical mechanics, or non-equilibrium physics. Experience in
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in condensed matter physics, materials science and engineering, or a closely related field completed within the last five years. Excellent track record in the physics of correlated materials
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learning algorithms in PyTorch. Expertise in object-oriented programming, and scripting languages. Parallel algorithm and software development using the message-passing interface (MPI), particularly as
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professional, academic, and research organizations. Basic Qualifications: A PhD in computer science/engineering or relevant area with an education and a research track record in HPC/AI/edge systems and storage
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experiments to neutron reflectometry studies. The main objectives are: (1) To implement fluorescence microscopy techniques and Flicker spectroscopy to studies of DIBs under electrical stimulation; (2) Use other