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. Proficiency in deploying and managing wildlife camera‑trap networks and processing large image datasets. Experience developing and validating machine‑learning and AI models for image object detection and
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of mobile ringtones. Traditional machine learning methods and transformer models will be used to learn patterns from audio signals and classify ringtones into predefined categories (e.g., default ringtones
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analysis, contextual analysis, audio feature extraction, and machine learning models to identify and assess potentially dangerous content. Similarly, computer vision models are implemented to analyse images
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learning models. with a proven ability to provide solutions within an agile environment and short-term development sprints. Excellent interpersonal skills, and a proven ability to manage a team, work well
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development. Expertise in Python programming and data analysis. Experience developing Machine Learning models. TensorFlow or PyTorch is desirable. How to apply To apply, please ensure you have digital copies
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experience using Python machine learning and large language models. Experience in machine learning and NLP for automated misinformation detection, social media data scraping and analysis, and human annotation
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science, immunology, cell biology) Experience in mouse models of disease and animal handling Experience in cellular and molecular immunology, including ELISA, FACS, ELIspot High-level computer and database
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University of Western Sydney | The University Of Sydney, New South Wales | Australia | about 2 months ago
of the University may be required. Please review the position description for further information. About You You hold a doctoral qualification in Cognitive Neuroscience, Machine Learning, Computer Science or another
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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instance learning and weak supervision / spatial transcriptomics models to individualise tumour type, associated biomarkers and genomic characteristics to high precision. The resulting multipurpose machine