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Field
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communication) Willingness to learn and confront new challenges Preferred Qualifications Doctoral research conducted in the area of machine learning for healthcare and related topics Deep knowledge of multi-modal
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ethical frameworks. Proficiency in Python and experience with relevant libraries for AI/ML development. Experience with advanced AI methodologies including deep learning, transfer learning, and neural
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learning, deep learning, and large language models (LLMs), for the analysis of high-throughput multi-omics datasets (especially single-cell and spatial omics) and large textual corpora (e.g., scientific
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related field Strong oral and written communication skills Demonstrated motivation, initiative, and attention to detail Deep interest in translational neurotechnology, medical device development, and
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interpretable deep neural networks is required. Candidate must have published in top journal and conference at least one scientific paper in interpretable machine learning (not explanations of black boxes) among
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CBS - Postdoctoral Position, Artificial Intelligence Applied to Metabolomics for Health Applications
metabolomics data from clinical studies. Apply deep learning models (e.g., autoencoders, variational autoencoders, graph neural networks) for biomarker discovery, disease classification, and patient
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at the intersection of innovation and tradition. Renowned for hands-on learning and pioneering research, Mines educates future leaders in STEM fields who will make a meaningful impact on the world. Our vibrant
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mission is directly tied to the humanity, dignity and inherent value of each employee, patient, community member and supporter. Our commitment to learning across our differences and similarities make us
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genetics/genomics/omics, or 2) deep learning/AI. Most importantly, we value candidates who demonstrate both the ability and drive to rapidly learn and implement recent advances in AI. Create a Job Match for
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Description Post-Doctoral Fellow Position in Medical Image Processing (Deep Learning for Trauma CT) The Trauma Radiology AI Lab (TRAIL) in the Department of Radiology & Nuclear Medicine at the University