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Field
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models using frameworks such as PyTorch and TensorFlow. Research experience in medical image analysis using deep learning algorithms. Strong track record in machine learning, computer vision, and medical
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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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Fellow will have the opportunity to lead statistical analyses of large-scale clinical registry and administrative datasets. Some key skills required: A PhD in health data science, and/or relevant work
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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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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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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
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Intelligence (AI) and machine learning, digital sociology, AI and the sociology of knowledge, AI and expertise, and digitalization and criminology (digital criminology). A prerequisite for employment is that
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Computer/Information Sciences Internal Number: A-168130-3 General Description Professor Gillian Hadfield is seeking a highly-qualified postdoctoral scholar to join her team at the Normativity Lab in
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mathematical modelling tools. Excellent knowledge of programming languages such as R, Python, Julia, etc. Familiarity with AI algorithms and Machine Learning Fluent oral and written communication skills in