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Field
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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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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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the role Overview of the role We are seeking a highly motivated Research Fellow in Machine Learning to join the PharosAI team, focusing on developing novel machine learning methods in computer vision
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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
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develop algorithms to identify and predict SRL subprocesses from multimodal learning data (e.g., EEG/fNIRS, eye-tracking, and think-aloud protocols); • Analyze large-scale learning analytics data
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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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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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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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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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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