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learning algorithms for engineering systems Programming experience in FORTRAN, C, or C++ and scripting experience in Python or similar languages Experience with parallel computing environments and Linux
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or autonomous tools within complex pipelines. Proficiency in modern AI frameworks and tools (e.g., PyTorch, TensorFlow, LangChain, MCP SDKs) and programming languages (Python, C++). Experience with provenance
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degree in physics or related discipline completed within the last five years. Preferred Qualifications: Experience with modern programming languages, particularly Python. Demonstrated ability
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Experience with numerical computing using programming languages such as C++, Python, etc. Author or co-author of open literature publications Special Requirements: Applicants cannot have received their Ph.D
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(HPC), or large-scale data analysis. Experience in applying AI/ML techniques to hydrological and Earth sciences. Proficiency in scientific programming languages such as Python, Julia, R, Fortran, or C/C
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Neuromorphic computing or Spiking Neural Networks Machine learning, AI/ML algorithms, or scientific computing Strong programming skills in Python and/or C++. Preferred Qualifications: Ability to work
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preferred; experience using Python, R, and/or OriginLab is highly desirable. Excellent written and oral communication skills. Motivated self-starter with the ability to work independently and to participate
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. Basic Qualifications: A PhD degree in civil, chemical, or environmental engineering. A minimum of 2 years of experience in the use of Python for programming of data analytical models and algorithms
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. Demonstrated expertise in writing advanced software in Python. Demonstrated experience with the LINUX operating system, LaTeX, Git, Python. Demonstrated expertise in the design and implementation of deep
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coding (Python) for building energy modeling and controls Preferred Qualifications: Expertise in modern optimal control techniques (e.g., AI based controls) High level of competence in coding and scripting