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strong publication record (first-author papers in high-impact journals preferred). Demonstrated expertise in at least two of the following areas: AI/machine learning for biological modeling (e.g., virtual
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new insights into the phenomena observed and enrich the databases required for deep learning methods. The neural networks currently being developed at LISTIC to detect and segment areas of movement in
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computational datasets of disordered materials based on density functional theory calculations and training machine learning models to accelerate the predictions. This work will involve collaboration with Assoc
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Associate Research Scientist / Post-Doctoral Associate in the Division of Science (Computer Science)
to develop applied research skills in machine learning, interact with an international network of collaborators, and gain post-doctoral research experience. The ideal candidate is self-motivated and can work
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. Applicants focused on topic 2 should be able to deal with logistical issues and challenging conditions for field campaigns in Alaska and Canada. Selected candidates will closely collaborate with other team
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communication skills, including the ability to translate statistical results for clinical audiences and to teach non-statisticians. Brief Description of Duties: The Postdoctoral Associate will work under the
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fields; proficiency in evaluating the effectiveness of learning materials; strong track record of working both independently and collaboratively; be organized, able to lead multiple projects simultaneously
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, integrate device engineering with clinical workflows, and apply artificial intelligence and machine learning for automated image and signal analysis, tissue classification, and real-time diagnostics
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representations • Research on multimodal representation learning for fusing heterogeneous clinical data (imaging, pathology, genomics, clinical text) into unified vector representations • Design and evaluation
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Associate will collaborate with instructors of Environment and Sustainability (E&S) core courses to: develop and integrate active-learning strategies that strengthen systems-thinking competencies; design