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Experience with deep learning frameworks such as PyTorch or TensorFlow Exposure to AI-enabled scientific workflows that couple simulation with data-driven modeling, including emerging approaches involving
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comparative research across Mojo, Julia, Rust, and vendor toolchains. Basic Qualifications: Ph.D. in Computer Science, Computer Engineering, or related field. Experience with LLMs or agentic AI frameworks
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Laboratory. Major Duties/Responsibilities: Develop and implement advanced time-frequency and spectral analysis techniques for high-frequency voltage/current waveform data Apply wireless PHY-layer concepts
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will involve designing beam dynamics experiments, measurement, simulation, and data analysis. This position resides in the Accelerator Physics Group in the Accelerator Science and Technology Section
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techniques; and (3) developing advanced methods for inelastic neutron scattering data analysis and workflow automation. The postdoctoral researcher will work in close collaboration with Dr. Raphaël Hermann and
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, mentorship of junior staff, and the ability to influence program direction through data-driven engineering judgment and recognized subject-matter expertise. Key Responsibilities: Independently lead and perform
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ferroelectric and ferroelastic materials, under external stimuli such as electric fields, light, strain, and temperature. This position resides in the Data Nanonanalytics (DNA) Group within the Nanomaterials
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Requisition Id 15813 Overview: We are seeking a highly motivated postdoctoral researcher with a strong background in sensor integration, data acquisition, and in situ process monitoring
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research spanning detector simulation, Spiking Neural Network (SNN) design, neuromorphic hardware, and data-rich experimental systems such as CMS pixel detectors, Timepix4, and novel photodetector
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challenges and conduct the research needed to accelerate the delivery of solutions to the marketplace. The Radiation Transport and HPC Methods (RTHPCM) Group within the Nuclear Applications Methods and Data