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Lagrangian transport models; • Expert knowledge of programming, preferably in Python and/or Fortran. What we offer: A longer-than-average postdoctoral contract with freedom to choose your specialized
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candidate holds a Ph.D. in Chemical Engineering (or a related field), is proficient in both mechanistic modeling and ML frameworks (e.g., TensorFlow, PyTorch), and has strong programming skills (Python/MATLAB
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candidate holds a Ph.D. in Chemical Engineering (or a related field), is proficient in both mechanistic modeling and ML frameworks (e.g., TensorFlow, PyTorch), and has strong programming skills (Python/MATLAB
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, ideally applied to thin-film growth by PVD. Skills in data analysis, programming (Python, etc.), and the ability to work at the interface between simulation and experimentation in collaboration with
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role in both the scientific application and open-source software development of UXarray, a Python package that extends Xarray to provide native support for horizontally unstructured computational meshes
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/Qualifications Strong research track record in AI and scientific applications. Excellent knowledge of Machine Learning and Deep Learning. Strong Python programming skills. Experience with PyTorch, TensorFlow
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),. Familiarity with data analytics, machine learning, digital twin knowledge, or Python programming language. Knowledge of additive manufacturing, process physics, thermodynamics, and/or metallurgy to interpret
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methods is a plus Programming experience in MATLAB and/or Python Familiarity with process simulation software such as Aspen Plus or Aspen HYSYS Application and Selection: The application folder must contain
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or program datasets. Proficiency in statistical programming and data analysis tools: R, Python, Stata, or SAS SQL/database management preferred. Ability to clean, harmonize, and analyze complex datasets from
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experimental work in chemical or process engineering (desirable) Knowledge of process optimization, parameter estimation, or control methods is a plus Programming experience in MATLAB and/or Python Familiarity