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Field
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oxygen extraction, see Ayton et al. Brain 2017, Biondetti et al. Neuroimage 2023, and our own publications: Chan & Marques, 2021, Marques et al., 2010. Where to apply Website https
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the JAX Python library, including efficient implementations of classical numerical algorithms. 2. Extend the hybrid FEA-ML framework to include nonlinear cohesive zone models with simple traction separation
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the JAX Python library, including efficient implementations of classical numerical algorithms. 2. Extend the hybrid FEA-ML framework to include nonlinear cohesive zone models with simple traction separation
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candidate with a PhD in ecology, ecotoxicology, or environmental modeling, and strong analytical and programming skills (e.g., R or Python). Experience with biodiversity metrics, ecosystem service modeling
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will benefit from the Jean Zay supercomputer to perform intensive GPU calculations but also MARBEC/LIRMM GPU shared resources (https://umr-marbec.fr/recherche/dispositifs-de-recherche/den/ ). His/her
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—and machine learning to work within established research programmes. A strong, demonstrable experience in research software development using Python programming language and modern machine learning
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quality (PM2.5, CO2, Nox, etc.) and energy monitoring (voltages, currents, battery charge/discharge cycles); Proficiency in languages commonly used in IoT, such as C/C++, Python, and JavaScript. Expertise
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or Departmental Website: https://med.stanford.edu/michellelinlab.html (link is external) How to Submit Application Materials: Please send materials to PI Dr. Michelle Lin (mplin [at] stanford.edu) with the subject
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programming languages such as Python is advantageous. Practical laboratory skills and an enthusiasm for experimental work are highly valued. Participation in teaching activities (e.g., supervising practical
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Python and/or MATLAB; familiarity with numerical optimization libraries and scientific computing. Strong written and oral communication skills; ability to work independently and in teams. Position Type