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to develop highly performant, CMOS compatible, thermal sensor and microbolometer technology. The project aims to revolutionize thermal imaging technologies for automotive, safety and surveillance by developing
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Duke University, Nicholas School of the Environment - Durham Position ID: Duke -NSOE-Durham -POSTDOC_SHINDELL [#30016] Position Title: Position Location: Durham, North Carolina 27708
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Bioinformatics and Computational Biology headed by Ivo Hofacker. Our team works on the development of algorithms and methods for problems in Computational Chemistry, Systems Chemistry, and Computational Biology
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high-dimensional, dynamic, networked system, applying techniques from machine learning, causal inference, statistics, and algorithms. No prior biomedical training is required—just strong quantitative
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devices, mutating enzymes for targeted functions, improving extracellular electron transfer efficiency, and testing of deployable bioelectrochemical sensors. The candidate will also be responsible
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work hands-on with clinical data and build robust deep learning algorithms. We welcome applications from individuals with experience in: Experience developing deep learning models for real-time image
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data to design robust, efficient deep learning algorithms that maximize the information extracted from images and delivered to the robot. To be successful in this role, we are looking for candidates
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machine learning. Essential Duties and Responsibilities: Develop and implement advanced reconstruction algorithms for correlated and low-dose imaging modalities. Maintain and extend Python-based software
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Duke University, Center for Advanced Genomic Technologies Position ID: Duke University -Center for Advanced Genomic Technologies -PDGENOME [#29957] Position Title: Position Location: Durham, North
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, the next step in this project is to address sparse optimization for tensors. We propose the integration of randomized algorithms into sparse optimization frameworks for the purpose of completing