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Position: Staff Deep Learning Engineer
Institution: Illumina, Inc.
Location: Cambridge, United Kingdom
Duties: Lead the development of novel deep learning for deciphering the human genetic code, diagnosing pathogenic genetic variants by combining information from detailed clinical phenotypes and medical records, protein structures, and genomic data; Lead scientific collaborations with Illumina’s academic, nonprofit, and industry partners, and lead a multidisciplinary team focused on achieving these aims; Publish and disseminate methods and findings and incorporate them into software products to the benefit of the wider genetics community
Requirements: MD or PhD in computer science, genetics, computational biology, or related field; Expert in deep learning, statistics, proteinomics and/or genomics; Possesses strong communication skills, with the ability to present complex scientific ideas to clinical, scientific, and industry audiences; Be willing to work in a fast paced, competitive environment, and hold a strong record of successful delivery of complex scientific projects and publications under tight timelines
   
Text: Staff Deep Learning Engineer Lead the development of novel deep learning for deciphering the human genetic code, diagnosing pathogenic genetic variants by combining information from detailed clinical phenotypes and medical records, protein structures, and genomic data; Lead scientific collaborations with Illumina’s academic, nonprofit, and industry partners, and lead a multidisciplinary team focused on achieving these aims; Publish and disseminate methods and findings and incorporate them into software products to the benefit of the wider genetics community MD or PhD in computer science, genetics, computational biology, or related field; Expert in deep learning, statistics, proteinomics and/or genomics; Possesses strong communication skills, with the ability to present complex scientific ideas to clinical, scientific, and industry audiences; Be willing to work in a fast paced, competitive environment, and hold a strong record of successful delivery of complex scientific projects and publications under tight timelines
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