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Field
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advanced biostatistics Computer proficiency, including use of statistical software for spatial analyses, such as ArcGIS, QGIS, R, Google Earth Engine Experience assembling, organizing, and analysing large
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the statistical analysis using SAS software or R; conduct literature reviews as necessary, prepare tables and figures, and participate in manuscript preparation and presentations; contribute to the development
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top-tier machine learning or computer vision conferences/journals, such as TPAMI, IJCV, CVPR, ICCV, ECCV, NeurIPS, ICML and ICLR. Knowledge of scientific or engineering software like PyTorch, TensorFlow
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: A PhD degree in Civil Engineering or Earthquake Engineering in a closely relevant field. Familiarity with earthquake data, models and software used in earthquake hazard and loss modelling
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software engineering. • Track Record: Strong publication record in leading machine learning, artificial intelligence, data science, or evolutionary computation venues (e.g., ICML, ICLR, NeurIPS, AAAI
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; lip-sync and audio-visual synchronization; evaluation of factuality, citability, WER, MOS and latency; software engineering, version control, containerisation or development pipelines; technical
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, Transformers, Python, PyTorch and/or TensorFlow, multimodal fusion); Active and semi-supervised learning; Software engineering for R&D (Git, reproducible environments, large-scale data pipelines). Exclusive
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, collect and analyze data using appropriate analytical tools and software. Work with interdisciplinary teams including clinicians to integrate microfluidics chips with complementary technologies, such as
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and progressive streaming; performance and visual-quality evaluation (P50/P95, LPIPS/SSIM, thermal stability and audio-visual drift); software engineering, version control, containerisation
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systems Working knowledge of ecological (especially landscape ecology) research methodology and advanced biostatistics Computer proficiency, including use of statistical software for spatial analyses