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description The candidate will work on problems at the intersection of mathematical statistics, machine learning, and generative modeling, particularly for sequential data arising in complex dynamical systems
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been recent rise in vision foundation models such as GPT-4o, Instruct-BLIP and LLava. However, training foundation models has significant environmental costs tied to large scale datasets as highlighted
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, machine learning models, and AI systems that address real-world challenges in banking and finance. Collaborate closely with CommBank representatives to understand industry needs and translate research
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programme. To acquire, analyse, interpret and evaluate research findings/data using approaches, techniques, models and methods selected or developed for the purpose. To establish a national reputation and
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discoveries. Who You Are: Ph.D. with a proven track record of excellence in Computer Science and Machine Learning, with substantial domain experience in biology and genomics. Must have advanced at least one key
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communication journals Demonstrable proficiency in advanced quantitative data analysis: applied machine learning, statistical analysis, and handling complex data. Programming skills in Python and R are essential
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(SaRRU) is seeking a highly motivated Research Fellow (RF) to join our multidisciplinary team. The RF will work on the research project "Modelling Self-Regulated Online Learning: Video- and Digital Game
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Language Processing (NLP) with a focus on large language models, deep learning, and multi-modal machine learning. The researcher will work on the project KAMAL Health: Knowledge-Augmented Multi-Modal Arabic LLMs
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 24 hours ago
of machine or deep learning models to identify patterns of codon usage in yeast genomes, 2) the implementation of tRNA-sequencing across diverse yeasts and conditions, and 3) the construction of tRNA deletion
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The idea is to combine established iterative ensemble Kalman methods with novel emerging machine-learning-enabled model calibration techniques recently adopted in CLM-FATES at UiO. The aim is: to constrain