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systems. This position is part of an Innosuisse-funded collaborative project with industry partners, focusing on the development and performance assessment of externally bonded CFRP reinforcements using
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University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences. Embracing varying opinions and perspectives
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science, and sustainability. Driven by innovation, sustainability, and interdisciplinary collaboration, the center aims to bridge research and practical applications for the development of a large spectrum
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atmospheric and climate science, particularly on subseasonal timescales, and experience using machine learning methods. The postdoc will be advised by Dr. Marybeth Arcodia and includes collaborative efforts
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techniques. This position also requires the ability to work in a highly collaborative environment with team members having a broad range of scientific and technical backgrounds and levels of experience. In
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institution of higher learning, that is committed to an educational system based on the highest standards of teaching and research in fields related to the sustainable economic development of Morocco and Africa
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the outcomes of SCC surgery. Job Responsibilities: As a PhD candidate, you'll focus on: Develop cutting-edge AI models: Train state-of-the-art deep learning models to segment SCC and healthy tissues using both
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publishing of results. (20%) Collaborate with other team members on various lab projects (20%) Minimum Acceptable Education & Experience: PhD in quantitative ecology, marine science, or related field Required
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Post-doctoral Position in AI Causal models for Synchrotron Anomaly Detection H/F This post-doctoral position is part of a collaboration between LIAD (Laboratory of Artificial Intelligence and Data
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crisis, by 2050, Texas's population is expected to reach 40 million, creating unprecedented demands on resources and infrastructure. Our program’s mission is to advance collaborative research on resilience