44 parallel-processing-"International-PhD-Programme-(IPP)-Mainz" Postdoctoral positions at Argonne
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from coal production including coal fines to produce graphite precursor materials. This separation will likely include processes such as froth flotation, gravity separation and leaching. Position
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pipelines, including pulse processing (e.g., optimal filtering), pileup mitigation, drift correction, and energy-scale stability. Design, propose, and execute high-impact in-house spectroscopy experiments
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The Chemical and Fuel Cycle Technologies division is seeking a Postdoctoral Appointee to join a multidisciplinary team developing electrochemical reactions and processes in molten salt electrolytes
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math, HPC, signal processing, computational physics and materials science. The appointee will benefit from access to world-leading experimental and computational resources at Argonne including some of
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scientists, roboticists. The project will focus on developing an integrated autonomous lab system for strucutre-property characterization of novel materials heterostructures for quantum and microelectronics
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processing and visualization. Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range for this position is $70,758.00
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and processing strategies aimed at achieving high performance, cost-effectiveness, and manufacturability. The selected candidate will leverage the capabilities of the Materials Engineering Research
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processing for accelerated discovery as well as investigations into approaches to harness chemical selectivity at material interfaces to react with and repair electronic defects. Position Requirements
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processing Strong background in ferroelectrics, photonics, or solid-state materials Demonstrated ability to work independently and collaboratively in a multidisciplinary environment Strong written and oral
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studies (e.g., EELS, EDS) to probe defect structures and dynamics Apply advanced image processing and analysis; develop AI/ML workflows for quantitative defect characterization Implement high-throughput and