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Field
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interpreting large datasets using statistical programming languages (e.g. R, Python etc.) Proven independent working style, technical problem solving, data analysis and generation of novel ideas Proficient
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research and developing decision support systems. The candidate would also assist in writing python/R programming scripts to optimize processing time, making the current decision support systems more
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machine learning. The work will require solid programming skills, especially in Python and Julia. We expect the candidates to have: MSc degree in physics, materials science, chemistry, mathematics
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simulation or programming tools such as MATLAB, Simulink, MATSim, AnyLogic, or Python. Applicants should also have a good knowledge of decarbonisation and sustainability principles in transport, energy systems
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confident in handling the relevant data and be competent in programming on either MATLAB or Python. You will also be enthusiastic about building and troubleshooting your experimental rig. You will have the
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with large-scale data construction and empirical research • Programming skills (R, Stata, or Python) Applications should be sent by e-mail, together with significant documents, indicating the reference
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) Programming skills in Stata, Python, or QGIS are a plus Familiarity with Overleaf (LaTeX) is a plus Strong organizational skills and the ability to meet deadlines Ability to work independently and as part of a
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 38 minutes ago
Tomography to verify entanglement fidelity. Strong proficiency in Python, MATLAB, or LabVIEW for automated instrument control and data analysis is expected. Preferred qualifications include familiarity with 1K
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mechanics and analysis Experience with the following: Structural health monitoring (SHM) Finite element modeling (e.g., ABAQUS, SAP2000, ANSYS) Machine learning / AI (Python, TensorFlow, PyTorch) Demonstrated
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programming skills in Python Experience with scientific Python tools such as pandas, scikit-learn, matplotlib, and Jupyter; experience with PyTorch is a plus Familiarity with microbiome data analysis