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Field
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of equity, diversity and inclusion. Desirable characteristics: Experience in large data sets and their platforms/tools, cloud-based architectures, and deployment frameworks for machine learning algorithms
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from sensors or other continuous data sources. Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages
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), arrest and/or conviction record, military or veteran status, sexual orientation, gender expression and/or identity, an individual’s genetic information, domestic violence victim status, familial status
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. Desirable: Proficiency in scientific programming (e.g. Python) and familiarity with data science and machine learning techniques. Experience with geochemical analytical techniques and working in a laboratory
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in statistics, machine learning, mathematical modelling, or a related field, to join our research team in the Department of Applied Health Sciences. The successful candidate will work on an NIHR funded
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using hybrid models combining mechanistic, GenAI, and machine learning approaches. You’ll contribute to building disease-specific Digital Twins using large-scale single-cell multi-omics datasets
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Computer Science, Biomedical Engineering, Pathology Informatics, or a related field, with emphasis on computer vision and machine learning (summer and fall graduates are also welcome to apply) Proficiency in
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field relevant to the interface of energy technology, economics, data science and public policy. Examples of relevant disciplines include (but are not limited to): any physical, mathematical, computer or
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strong, demonstrated interest to conduct academic research in a relevant field Interest in legal research Interest in causal inference and social science Experience with machine learning / deep learning
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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