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ATAC sequencing, spatial transcriptomics, proteomics, whole-genome sequencing, functional screens, bioinformatics, and/or data algorithms including machine learning will be given preference. A successful
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multimodality imaging including PET/CT, SPECT/CT, and PET/MRI with a focus on integrating machine learning techniques. The appointment will be two years from the date of hire with a possibility of extension
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be disseminated through academic publications and online webinars. The successful candidate will have a PhD in human-computer interactions or computer science and related fields, with demonstrable
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single synthetic program of computational geometry. Specific interests include morphology, design topology, discrete differential geometry, packings, and machine learning methods for unstructured geometric
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in the health sciences, including fields such as healthcare informatics, movement and rehabilitation sciences, medical imaging, remote sensing, computer vision, mental health, data fusion
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platform that utilizes deep learning to analyze images of bruises. Responsibilities: Responsible for developing components of the project platform or deep learning application; Supervises a team of graduate
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Experience with high performance computer clusters (e.g, TAMU-HPRC, UT-TACC, NVIDIA Data Center). Preferred Qualifications Background in estuarine ecology, aquatic vegetation Experience with image analysis
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algorithm development, modeling machine learning, and scientific simulation ▪ Ability to work well in an interdisciplinary environment, and to collaborate with experimentalists ▪ Strong oral and written
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midlife brain function and cognition. The applicant should be interested in applying multivariate and machine learning neuroimaging methods to the study of how chronological ageing, biological sex, and
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) The Centre for Quantum Technologies (CQT) in Singapore brings together physicists, computer scientists and engineers to do basic research on quantum physics and to build devices based on quantum phenomena