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Field
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domain in the design of deep learning algorithms for cardiovascular disease detection. 4. REQUIRED PROFILE: Admission requirements: Master’s degree in Biomedical Engineering, Computer Engineering
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are template based and all US measurements are auto-populated into templates for increased accuracy and efficiency. Fourteen fellowship-trained, subspecialized expert faculty perform both image interpretation
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INESC TEC is accepting applications to award 4 Scientific Research Grant - NEXUS - CTM (AE2025-0564)
systems; - experience in applying Artificial Intelligence/Machine Learning and/or optimization algorithms to wireless networking systems.; Minimum requirements: The four Research Initiation Grants to be
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fields. Relevant Training or Courses (optional): Experience with R or similar programming tools, or machine learning algorithms. Geographic Information Systems (GIS). Basic and advanced statistics, time
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at https://puwebp.princeton.edu/AcadHire/position/40281 and submit a current curriculum vitae, research statement, and a cover letter. Contact information for three references is required. To learn more
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economic assessments machine learning or proxy-model based methods field scale simulation geological features geomechanics reactive flow The PhD fellow are not expected to master all these topics. Project
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, Effects, and Criticality Analysis (FMECA), functional FMECA, advanced sensing techniques, sensor and operational data fusion, data analytics, and machine learning algorithms for condition monitoring, fault
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references is required. To learn more about AI at Princeton, please visit https://ai.princeton.edu. Princeton University is committed to fostering a diverse and inclusive academic community. To maximize
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solving complex problems at the intersection of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical, IoT systems. Key
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, friendly and inspiring, and the position represents a unique opportunity for career development for a hard-working candidate. Main responsibilities Develop and apply machine learning and statistical modeling