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The candidate will have a PhD or equivalent degree in bioinformatics, biostatistics, computational biology, machine learning, or related subject areas Prior experience in large-scale data processing and
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models
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annotation of these metabolomes using multistage fragmentation (MSⁿ) data, incorporating novel computational methods and strategies (e.g. spectral matching, network-based approaches, machine learning) where
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Canada Excellence Research Chair in Sustainable Agriculture for Grape and Wine Professor or Associat
Nature Careers | Government of Canada Ottawa and Gatineau offices, Ontario | Canada | about 6 hours agoUniversity Brock University is committed to building inclusivity and equity through understanding and respect for diverse identities. These commitments are reflected in our approaches to teaching and learning
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information systems engineering. The group conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities and Machine Learning/AI on organisations from both
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information systems engineering. The group conducts research on the application and the impact of digital technologies like DLT/Blockchain, Digital Identities, Machine Learning/AI, GenAI, and IoT/5G
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managing large amounts of data by designing structured databases (PostgreSQL, MySQL). Machine learning methods such deep learning for analysis of proteomics data and classification of cancer profiles. Since
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or infrastructure. This is what makes our daily work so meaningful and exciting. The Division of Computational Genomics and Systems Genetics is seeking from October 2025 a PhD Student in Deep Learning for Rare
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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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sciences. Areas of interest include but are not limited to: AI-driven drug discovery and development, quantitative systems pharmacology, large language models for education and clinical support, machine