59 programming-language-"St"-"University-of-St"-"St" Postdoctoral positions at Argonne
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programming language and contributions to open-source scientific software Good scientific productivity, as demonstrated by publications and conference presentations Effective oral and written communication
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fluorometric profiles of cyanobacteria using devices such as the Beckman Coulter Cytoflex Bioinformatics – Basic scripting experience in languages such as Python, R or BASH to carry out rudimentary genomics
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The successful candidate will be highly motivated and have a strong track record in problem solving and scientific publications. The candidate will be expected to conceive of, plan, and implement scientific
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(MSD), and Quantum Information Science (QIS) programs Disseminate results through high-impact publications and presentations at internal and external meetings Position Requirements Position Requirements
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electrocatalyst materials Plan and execute in situ/operando studies using advanced techniques such as X-ray Absorption Spectroscopy (XAS), X-ray Photoelectron Spectroscopy (XPS), Raman spectroscopy, Differential
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linear, mixed-integer, and stochastic programming. Work with programming languages such as Python, Julia, or C++ to build robust analytical tools and perform large-scale data analysis. Collaborate with
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Demonstrated experience with HEP or NP detector design and simulation Proficiency in scientific software development (e.g., C/C++, Python, or similar languages) Ability to model Argonne’s core values of impact
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interpersonal, written, and oral communication skills. Experience with molten salt systems, actinide chemistry, or materials science is desired, but not mandatory. Experience working safely with hazardous
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beamtime. Skilled written and oral communication skills at all levels of the organization. Proven ability to work both independently and collaboratively in a multidisciplinary environment. Flexibility to
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experience in economic and supply chain analysis, computational modeling, or policy analysis. Proficiency in scientific programming languages (e.g., Python, R) and data analysis libraries (e.g., pandas, NumPy