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. Key Responsibilities Lead AI/ML algorithm development for predicting plant water and nutrient uptake under varying environmental and growth conditions. Analyze multi-source data, including aerial and
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degree in Computer Science, Math, Statistics or Engineering programs. Ph.D would be an asset. Knowledge of classification algorithms, vision-language models (VLM) and Hugging Face Transformers is an asset
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: Course number and title: MIE1624F/S – Introduction to Data Science and Analytics Course description: The objective of the course is to learn analytical models and overview quantitative algorithms
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University of British Columbia | Northern British Columbia Fort Nelson, British Columbia | Canada | about 1 month ago
(Introduction to Software Engineering), CPSC_V 314 (Computer Graphics), CPSC_V 317 (Introduction to Computer Networking), CPSC_V 319 (Software Engineering Project), CPSC_V 320 (Intermediate Algorithm Design and
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objective is to develop a next generation of AI approaches that are more sustainable and accessible. Relevant domains include mathematical and computational optimization, learning algorithms, statistical
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coding using R and adeptness in using RStudio for data analysis. Produce comprehensive reports and presentations in PDF and HTML formats using R Markdown. Knowledge of machine learning algorithms
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reports and presentations in PDF and HTML formats using R Markdown. Knowledge of machine learning algorithms. Proficiency in Canvas and content organization. Excellent communication skills. Responsible and
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power of data science and algorithmic research with the fields of democratic theory, political science, and public policy. Ideally, the candidate has expertise and interest in innovative research using
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will design and implement scalable software infrastructure and data processing software for CHIME's massive datasets. The role includes algorithm development and data analysis of cosmology datasets
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. The ultimate objective is to develop a next generation of AI approaches that are more sustainable and accessible. Relevant domains include mathematical and computational optimization, learning algorithms