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flow chemistry, microfluidic reactions, reaction scale-up, calorimetry, microwave reactions; AI/ML; design of experiments, in-line analytics. Ability to communicate effectively and on a timely basis
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The Argonne team is seeking two highly motivated postdoctoral researchers to help shape the next generation of secure, scalable, and continuously learning AI systems for biomedical discovery. This
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The Data Science Learning division is seeking a postdoctoral researcher to develop and apply artificial intelligence and computational biology methods to investigate the molecular and cellular
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ideal for someone who enjoys working at the intersection of data science, machine learning, materials research, and experiment, and who is motivated to translate computational advances into real
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, benchmarking and deploy them for applications. The ideal candidate would be a PhD in geophysical sciences, computer science, or machine learning with experience in developing and verifying deep learning
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Argonne National Laboratory to advance learning-enabled imaging methods. This position offers a unique opportunity for candidates with backgrounds in electrical engineering, computer science, applied
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behavior of soft materials under operating conditions. The successful candidate will implement state of the art molecular dynamic simulations using machine learning potentials and apply free energy methods
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backscatter diffraction, focused ion beam specimen preparation, and computer vision or machine-learning analysis of microscopy datasets. The position requires strong experimental, analytical, written, oral
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Postdoctoral Researcher in artificial intelligence and machine learning (AI/ML) for advanced tuning and diagnosis of particle accelerators. The Accelerator Operations and Physics (AOP) Group of the
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closing this gap. The successful candidate will work in the Data Science and Learning division of the Computing, Environment, and Life Sciences directorate of Argonne National Laboratories. Primary