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data acquisition, data analytics, statistical modeling, and machine learning in manufacturing environment. Proficiency in Python and common data science and machine learning libraries (e.g., NumPy
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 2 months ago
Experience Strong background in machine learning, deep learning, or related AI techniques. Proficiency in Python and at least one major deep learning framework (e.g., PyTorch, TensorFlow). Evidence of research
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review) Proficient programming experience in Python and libraries (e.g., Pytorch, TensorFlow) with several years of practice Experience in maintaining high-quality code on Github Experience in running and
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data analytics, Python, and an affinity for acquiring new skills in this area. Ability to collaborate with both scientific and industrial partners. TU Delft (Delft University of Technology) Working at TU
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. Performs advanced signal processing and time–frequency analyses of neural recordings. Develops and implements computational pipelines using MATLAB, Python, or similar platforms. Leads manuscript preparation
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annotation, comparative or population genomics, command-line work in a Linux/HPC environment, and scripting in at least one of Python, R, or Bash. Hands-on molecular biology (DNA extraction, library
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analytical and quantitative skills, including statistical programming (e.g., R, Python, or similar). Knowledge of causal inference methods and/or comparative risk assessment approaches. Familiarity with
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interested in mentoring and supporting MSc and PhD students. You are a machine learning enthusiast (and realist). You love coding and have proven experience in e.g. Python, Matlab, JAVA, C#. You can present
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engineering, or a related field, with strong skills in mathematical optimization and modelling (ideally in Python) and an interest in sustainable urban mobility. Job requirements PhD in OR and other related
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-field fracture modelling, contact and micromechanics of particulate or composite materials, mechanical testing and X-ray tomography, and programming (e.g. Python, C++ or Fortran). An innovative mindset