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Field
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discovery. The successful candidate will develop new, openly accessible datasets and machine learning models for modeling redox-active solid-state materials. Candidates who are nearing completion
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, Lancaster and Sumter enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service
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Sumter enable students to earn associate or bachelor’s degrees through a combination of in-person, online or blended learning. All of our system institutions place strong emphasis on service — helping
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Laboratory seeks a postdoctoral appointee to join a multidisciplinary team developing complex systems models, including agent-based models, and new algorithms and tools for machine learning and optimization
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., transcriptomics, proteomics, metabolomics) and artificial intelligence/machine learning (AI/ML) applications in biomedical research will be considered a strong advantage. Outstanding UA benefits include health
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robotics, and human-machine interfaces. We are looking for an ambitious researcher who is looking to use this opportunity to grow towards the next level of research. The position is renewable each year based
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 1 hour ago
. Description: The post-doctoral fellow will apply machine learning and/or other data science techniques to conduct scientific research, improve remote sensing data products and analysis, integrate machine
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integrated circuits (IC) and printed circuit boards (PCB). Additionally, the candidate should demonstrate expertise in applying computer vision, image analysis techniques, machine learning, deep learning to IC
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. Research Themes and Projects: We are an interdisciplinary research team integrating single-cell and spatial genomics, lineage tracing, synaptic proteomics, functional perturbation screening, and machine
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accelerate translational research. Build or leverage novel artificial intelligence and machine learning (AI/ML) techniques for advanced liquid biopsy (ctDNA) and tissue transcriptomic (RNA-seq) data to predict