51 programming "https:" "https:" "https:" "https:" "https:" "Data driven Materials Modeling" positions at Argonne
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communication skills, as well as the ability to work effectively in a multidisciplinary research environment. Major Action and Supporting Actions : Conducts experimental programs in in-situ and ex-situ ion
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to a collaborative, multidisciplinary research program focused on the chemical recycling of polymers and organometallic catalysis. You will work alongside scientists with diverse expertise to design
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breakthroughs in NV sensor synthesis and host diamond heterointegration. The successful candidate will operate at the interface of these programs, playing a central role in developing and optimizing next
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optoelectronics. The successful candidate will build and lead an innovative research program in microelectronics by leveraging CNM’s unique strengths in materials synthesis, device fabrication, and characterization
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. This position requires close collaboration with Argonne researchers and external principal investigators to support the timely execution of research programs. Responsibilities also include preparing technical
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contributing to multidisciplinary projects and externally sponsored programs. The scientist will produce high-impact research, broadly useful software and systems capabilities, and sustained collaborations
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, assemble, operate, and troubleshoot bench- and pilot-scale chemical process equipment Plan and execute experiments related to separation processes, fluid handling, and process intensification Collect
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facilities, including synchrotron and neutron-scattering centers. The position offers the opportunity to establish and lead an independent research program while working closely with scientists across the QSCM
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of physical sciences, or in math, computer science, and electric engineering who have an interest in accelerator physics will also be considered. Strong programming skills. Proficiency in the Python programming
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. In this role, you will lead a research program centered on AI-driven autonomous synthesis, including: Active learning and Bayesian optimization over synthesis parameters such as precursors, temperature