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-aware multi-modal deep learning (DL) methods. At Argonne, we are developing physics-aware DL models for scientific data analysis, autonomous experiments and instrument tuning. By incorporating prior
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The Advanced Grid Modeling group at Argonne National Laboratory's Center for Energy, Environmental, and Economic Systems Analysis is seeking a dedicated Postdoctoral Researcher. This role is ideal
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-be-completed PhD (typically completed within the last 0-5 years) in chemistry, chemical engineering, material science, or related fields. Demonstrated knowledge in materials synthesis and
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publications, conferences, and internal reports Provide mentorship and guidance to graduate and undergraduate researchers involved in related projects Position Requirements Recent or soon-to-be-completed PhD
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are seeking an postdoctoral appointee to contribute to this research to understand the underlying physics of spin and charge based memory materials using advanced in-situ transmission electron microscopy (TEM
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Energy Systems and Infrastructure Analysis Division. We are seeking applicants with a strong technical background and expertise in international trade modeling, particularly in the upstream automotive
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, and optimize for energy efficiency HPC applications and high performance data stream analytics workloads. Use of novel accelerator designs, and automatic methods to model/predict how performance would
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, and spatial transcriptomics. Key responsibilities include: Developing AI/ML methods for image alignment across modalities Automated feature detection Predictive modeling of vascularization patterns
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, and safe laboratory practices. Position Requirements Recent or soon-to-be-completed PhD (within the last 0-5 years) in the field of Chemistry or a closely related discipline Demonstrated expertise in
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your PhD in computer science or engineering, the physical sciences, or a related field within the last five years. Comprehensive programming proficiency, preferably in Python. Experience with machine