Deep Learning for Taxonomic Classification Intern – Corteva Agriscience
Overview Corteva AgriscienceTM is seeking a student for an internship in our Global Farming Solutions & Digital group. The candidate selected for this position will be working at our research site at the University of Illinois Research Park at Champaign, IL. This position is only open to currently enrolled students of the University of Illinois – Urbana/Champaign* Title: Deep learning for taxonomic classification using genome sequence data Project Goal/Background: Taxonomic classification of modern-day genome sequence data entails the identification and assignment of each genomic read to their taxon-of-origin. The task is tedious and strongly biased towards available sequence information and known phylogenetic diversity we used as reference. This often results in generating inconsistent and incomplete taxonomic profiles leading to the misclassification of a given biological sample. Furthermore, incomplete taxonomic profile limits our ability to discover unknown biological species that can be critical for novel active/lead(s) discovery. Deep learning in the area of taxonomic classification has started to emerge as a more reliable alternative to the conventional sequence-based approach. The goal of the project is to evaluate deep learning approaches, including natural language processing, and develop a prototype application for taxonomically classifying a large number of biological samples using next-generation sequencing data. Deliverable(s) and Impact: The project will advance taxonomic classification capabilities by delivering deep learning models and applications to profile a given biological sample with higher accuracy and precision. Skillset of the desired candidate: The ideal candidate should be enrolled in a Ph.D. program (2nd or later) preferably in computer science, bioinformatics, or related quantitative field with exposure to course work in the areas of linear algebra, statistics, machine learning, and optimization algorithms (a plus). The candidate should have intermediate to advanced experience with machine learning and deep learning algorithms and concepts. Also essential is the ability to code in Python, R, and experience with working in Linux environments. Additionally, the candidate is keen on working in a team, has the willingness to learn new things, and possesses good time management and communication skills. Prior experience with optimization algorithms, and a knowledge of molecular biology is a plus. Expected hours/week: 40 hours per week** Reply to and send a current resume to Achal Rastogi, achal.rastogi@corteva.com *You must reference the job Title: Deep learning for taxonomic classification using genome sequence data in your response. Application Deadline: February 28, 2022, About Corteva Corteva Agriscience™ is the only major agriscience company completely dedicated to agriculture. By combining the strengths of DuPont Pioneer, DuPont Crop Protection and Dow AgroSciences, we’ve harnessed agriculture’s brightest minds and expertise gained over two centuries of scientific achievement. At Corteva Agriscience™, we are driven by our beliefs and our purpose, which is to enrich the lives of those who produce and those who consume, ensuring progress for generations to come. * If you are enrolled in the Fall 2022 semester but not taking summer coursework you can be considered eligible. Proof of enrollment must be presented if requested. ** Summer semester 40-hour work week is preferred but you may negotiate lesser hours if offered but are eligible to work up to 40 hours a week.
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