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Field
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Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related disciplines. Knowledge of autonomous vehicles or cyber security will be
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | 2 months 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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machine learning is essential; while structure prediction or materials chemistry experience would be advantageous, it is not a pre-requisite for the role. This post would be ideal for an ambitious and
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. Robert Mann (University of Waterloo), as well as a team of HDR students. The research will explore quantum aspects of physical black holes, collapse models, and the properties of ultra-compact objects
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the appointment. The candidates should have a strong track record in at least one of the group’s research areas, preferably demonstrated by publications in high-impact venues. Experience with machine learning
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comprise both the development of bioinformatics pipelines and the application of novel machine learning methods for interpreting microbiome and host ‘omics data from faecal, intestinal biopsy and saliva
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Computer Science or a related field Proven ability to conduct independent research with a relevant publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal
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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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an opportunity for a Postdoctoral Fellow. You will contribute to UNSW’s research efforts in developing machine learning and deep learning algorithms for dynamic systems (sequential or time-series data). Experience
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and energy materials. Preference will be given to those with knowledge of computer programming, AI or machining learning. Applicants are invited to contact Prof. Jianguo Lin at telephone number 2766