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Natural History. The researcher will develop deep learning models to predict individual bee age based on wing morphology. This model will be trained of existing wing images and applied to images of museum
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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA
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: Microbiome; Bacteria; Microbiology; Metabolites; Nuclear Magnetic Resonance, Mass-spectrometry, Chemometrics; Multivariate statistics; Artificial Intelligence (AI), Machine Learning (ML) and Deep Learning (DL
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following areas: Strong foundation in machine learning, optimization, and deep learning algorithms, including Transformer architectures. Hands-on experience or solid theoretical knowledge of LLMs/SLMs
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investigate deep learning architectures capable of learning microstructure-property mappings, including convolutional neural networks for microstructure image analysis, graph-based representations
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for Machine Learning (AIML) is the largest university‑based machine learning research group in Australia and the country’s first institute dedicated to advancing machine learning, computer vision, deep learning
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with Customer Relationship Management (CRM) or talent management systems. Knowledge, Skills, and Abilities (KSAs) Deep knowledge of experiential learning models, e.g., internships, co?ops, clinical
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candidates in the area of Business Analytics, with particular interest in those applying machine learning and deep learning methods to business domains, such as HR analytics, marketing analytics, and
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the Job which include, but are not limited to, Computer Vision, Deep Learning, Federated Learning, and Cloud Computing. Desirable: B1 A comprehensive and up-to-date knowledge of current issues and future
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engineering or similar. Knowledge and experience with deep learning models applied in computer vision. Remarkable academic trajectory, validated by a strong record of publications in relevant international