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/ Data Analytics Artificial Intelligence, Machine Learning and Autonomy Appl Deadline: 2026/06/17 11:59PM (posted 2025/06/17) Position Description: Apply Position Description The Skinnider Lab
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experience in life cycle assessment (LCA) and related tools for managing large data sets to evaluate natural resources needed to advance emerging technologies. The candidate will lead their primary project and
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machine learning methods in the context of biological systems Experience with programming (e.g., Python, Perl, C++, R) Well-developed collaborative skills We offer: The successful candidates will be hosted
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tools are need during the development of new imaging and sensing systems. With the rapid deployment of data-driven methods, repliable uncertainty quantification remains a big challenge that requires
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experience in Oxford Nanopore Technologies (ONT) sequencing and bioinformatics A track record of research in microbiome science, metagenomics, whole genome sequencing, big data analysis, machine learning, and
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in: Udder health and animal welfare Digital learning and employee education Big data and tech in agriculture Bilingual communication (English & Spanish a plus) This position is available now. If you're
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. Contribute to the development of research grants for funding of lab training and research. MINIMUM QUALIFICATIONS PhD in neuroscience, neurobiology, machine learning, biomedical engineering, or related field
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challenge. This project aims to explore data-driven Artificial Intelligence/Machine Learning (AI-ML) approaches to meeting this challenge. Possible topics include, but are not limited to: storylines
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worldwide, leveraging industry-standard tools and technologies to ensure the quality and reliability of the developed prototype hardware implementation. Qualifications: PhD in Electronics/Computer Engineering
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didactic skills High written and oral expression skills Computer user skills Excellent command of English Ability to work in a team We also expect: Teaching experience / experience with e-learning Experience