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with scientific programming and quantitative data analysis, particularly in Python, is welcomed. Experience with droplet generation, thermal diagnostics, image processing, or automated experimental
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Instituto de Geografia e Ordenamento do Território da Universidade de Lisboa | Portugal | 18 days ago
Mobility in Neighbourhoods with Different Levels of Accessibility" (Project Reference: DUT/0007/2022), funded by the Foundation for Science and Technology (FCT, I.P.). - DOI:https://doi.org/10.54499/DUT/0007
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should have a good knowledge of scientific programming (e.g., Python, C, Fortran) and finite-element or finite-difference schemes. It will be an asset if the candidate has prior research experience in
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learning materials for researchers at different levels of technical experience. Advanced AI for research: Provides instruction and consultation on large language models, prompt and context design, model and
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learning methods, with strong analytical and programming skills (e.g. R or Python) Experience of undertaking accurate, reproducible data preparation and analysis A track record of peer-reviewed research
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infrastructure over time, under different scenarios. Expected Results: 1) Development of geometric deep learning, time-series data mining, and agent-based approaches incorporating microbial information for DWDSs
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University of New Hampshire – Main Campus | New Boston, New Hampshire | United States | about 7 hours ago
modeling and environmental data analysis, such as Python, MATLAB, Fortran, C/C++, or comparable languages and tools. Ability to work with large datasets and to integrate these data with acoustics models
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related field. You have a strong background in numerical modelling and programming (preferably Python). You will collaborate with colleagues who are AeoLiS and CFD specialists, so affinity with
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, Python-based proof-of-concept. Develops and maintains robust software (primarily in Python) for DICOM tag de-identification and replacement, folder reorganization, and automated data quality control and
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) these effects can contribute significantly to local dynamics and their contributions are often under-estimated using global models. Previous work has shown that ionospheric mesoscale flows vary at different