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, especially. multimodal data analysis. Experience on image processing (especially on MRI data) via machine learning. Programming skills (e.g., Python) are required. Ability to communicate effectively in both
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skills, including proficiency in statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets
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. 3. Machine Learning and Predictive Analytics: • Develop and apply machine learning models (including Azure Machine Learning) to optimize healthcare data analysis accuracy. • Collaborate with data
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computing systems design and realization, including machine learning (ML) and artificial intelligence (AI) applications including autonomy, sensing and communication, advanced manufacturing, and decision
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Functions Developing and implementing machine learning and deep learning models to analyze forestry, physiological, and ecological datasets Modeling plant growth, carbon allocation, stress response (e.g
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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scientists, and machine learning experts will be an essential and enriching component of the position. Strong candidates will have a background in machine learning and natural language processing (NLP), with a
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● Experience with machine learning, AI, statistical modeling, or data analysis, demonstrated through publications, projects, or dissertation work ● Track record of completing research projects, as
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qualitative and quantitative analytical methods to model clinician attention, verbal reasoning, and documentation behaviour Develop and evaluate machine learning models, including unimodal, fusion, and
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for 10-12 weeks. Responsibilities: Collect and organize different datasets. Derive summary statistics of those datasets. Help with the implementation of machine learning models. Conduct literature