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schools in the world. For more details, please view https://www.ntu.edu.sg/mae/research . Key Responsibilities: Develop and implement high-fidelity CFD and FEA simulation workflows for modelling heat
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Python and AI-enabled methods to extract, classify, and summarize insights from unstructured data, including text, images, and other digital content. This work will help BSA detect trends, measure impact
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with electrophysiology acquisition and analysis (for example spike sorting, LFP analysis, population analyses) Strong quantitative skills and programming experience (MATLAB and or Python), including
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Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial | Portugal | about 6 hours ago
). Experience in machine learning methods, including data preprocessing, model selection, and performance evaluation. Familiarity with programming languages commonly used in these domains (e.g., Python, MATLAB
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collaborators. For additional information about the Liao research group, please visit: https://sites.google.com/view/chentingliao/ Department Contact for Questions: Questions regarding the position or application
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, progression, and therapeutic response. This research is fundamental to advancing our knowledge of cancer and improving patient outcomes. See further information at the lab webpage: https://odin.mdacc.tmc.edu
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(for example spike sorting, LFP analysis, population analyses) Strong quantitative skills and programming experience (MATLAB and or Python), including reproducible analysis workflows Familiarity with
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experiment codebases (prefer Python). Apply causal inference and discovery frameworks to clinical questions. Translate proposed methods and frameworks into real-world clinical workflows. Contribute to grant
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environmental and ecological data; knowledge of, or interest in, statistical data analysis using programming tools (R, Python, MATLAB, or equivalent); and the ability to work collaboratively in interdisciplinary
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(Kubernetes), serverless computing, and REST API development. Proficient in Python, with basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP