68 image-processing Postdoctoral positions at Conservatorio di Musica "Santa Cecilia"
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awareness, are known to be critical. However, the role of other factors – such as differences in visual processing and executive functions (EFs) – are still debated. The overarching goal of this project is to
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. Qualifications for this position include a PhD in Computer Science, Artificial Intelligence, Natural Language Processing, Human-Computer Interaction, or a closely related field. Candidates should have demonstrated
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metabolism, redox biology, and tissue homeostasis using advanced MRI and MRS techniques, including dynamic redox imaging and spectroscopy-based metabolites' evaluation. The candidate will work with preclinical
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is $76,383. The Water & Energy Efficiency for the Environment Lab (WE3Lab) seeks an entrepreneurial Postdoctoral Scholar with a vision for the coordinated operation of water and electricity grids
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. Investigate polypharmacy and multimorbidity in newly diagnosed patients with autoimmune rheumatic diseases. Contribute to studies utilizing natural language processing (NLP) to assess patient self-management
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Required Application Materials: Timeline: Application Deadline: April 3, 2025 Selections to be made by May 2025. This is a 1-year appointment starting Fall 2025. (Applicants advancing in the review process
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, electrical engineering, experimental physics, or a related field Strong programming and signal processing skills, with experience in Python and/or MATLAB Demonstrated ability to work independently and
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process modeling to understand care delivery Specific responsibilities and research projects will be tuned to the career goals, technical strengths, and interests of the applicant. Required Application
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research experience. Research track record should be demonstrated via prior publications in relevant venues in psychology, cognitive science, education, or human-computer interaction. Knowledge and expertise
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language processing (NLP) to augment clinical decision-making and expand access to high-quality healthcare. Our lab develops new methods to improve model trustworthiness and leverages heterogeneous clinical data