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Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
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of bioacoustic signals. Extensive experience with programming (Matlab, R, Python) including GPU programming is required, and familiarity with edge-based machine learning (particularly sound event detection), open
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Engineering, Machine Learning, Applied Mathematics, or a related field. A strong academic background and interest in AI systems, embedded intelligence, edge computing, machine learning, or related areas. Strong
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Assistant Professor Position in Transient Electromagnetic Signal Processing, Modelling and Inversion
machine learning, multidimensional inversion, and probabilistic geological modelling to enable efficient mapping in previously inaccessible terrains. The successful candidate will be employed primarily in
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data integration and analysis Integrate phylogenomic and functional data using machine-learning approaches for candidate gene prioritisation Contribute to software and web-tool development Present
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experience with natural language processing and/or machine learning (e.g., through first/co-authored publications) Demonstrated interest in interdisciplinary research at the intersection of AI and law
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analysis, causal inference with machine learning, and deep learning for various health-related domains. The Global Pathogen Analysis Platform (GPAP) is a new international initiative to strengthen global