Student Employment & Internships

The Part-Time Student Program at the AbbVie Innovation Center is designed to augment AbbVie’s regular staff, and is for students who are able to work in parallel to their school schedule Major duties for this position include: Working with the digital health team to evaluate a variety of digital sensors and consumer wearable, smartphone apps in regards to their sensor quality and features suitable for human subject research and clinical trials. The scientist will be responsible for authoring the evaluation reports/publications. Qualifications Must be registered as a full-time student Must be able to commute to the work location for each shift Graduate students (PhD candidate or post-doc) Cumulative minimum GPA of 3.0/4.0, or equivalent Must be authorized to work in the US on a permanent basis without requiring sponsorship 12 – 15 hours/week during the spring semester 35 – 40 hours/week during the summer term Physics or engineering or advanced analytics background Demonstrate the ability to construct a research paper, understand the first principle problem-solving framework, detail-oriented in documentation Experienced in sensor signal processing, particularly with familiarity in the triaxial data, gyroscope, image analysis, app development Software skills – MS Office Communication skills – verbal, written, report, publication Ability to produce detail documentations throughout the projects. Willing to learn R, Python, MedLab, SAS statistics, data modeling Timeliness, Self-motivated Note: The position will be hired through a third-party hiring agency and will begin as soon as possible over Spring 2020 and continue into Summer 2020. Apply through Handshake Who We Are AbbVie (NYSE:ABBV) is a global, research-based biopharmaceutical company formed in 2013 following separation from Abbott Laboratories. The company’s mission is to use its expertise, dedicated people and unique approach to innovation to develop and market advanced therapies that address some of the world’s most complex and serious diseases. AbbVie employs approximately 30,000 people worldwide and markets medicines in more than 170 countries. Learn more about us at www.abbvie.com The AbbVie Innovation Center is located in the Research Park at the University of Illinois at Urbana-Champaign and provides an opportunity for students and AbbVie’s Research and Development IT group to collaborate on projects to help advance our science.

The State Farm® Research and Development Center is a place where interns complete innovative, meaningful research in a collaborative environment focused on developing technical and business skills – a perfect complement to the classroom. Interns provide analysis and solutions through: Assessment of risk and financial needs for insurance products Development of innovative methods and applications to enhance insurance product pricing Data mining and predictive modeling such as regression, forecasting, and machine learning Research on emerging industry trends and hot topics Criteria: Actuarial Science, Math, Statistics, Data Science, or Computer Science majors preferred Prefer 3.5 minimum overall and major GPA Prefer experience with data and statistical analysis in Excel, SQL, SAS, R, and/or Python Strong communication, interpersonal, organization, leadership, and critical thinking skills Hours Spring/Fall: 10-15 hours/week for 16 weeks during the semester Location Convenient on-campus location with easy access to Campus Shuttles: State Farm Research and Development Center at the University of Illinois Research Park 2001 South First St. (Suite 102), Champaign, IL 61820 Additional Details The internship program provides opportunities for year-round positions for all student levels, freshman – graduate students. We offer development through extensive training programs, leadership, and rotational opportunities as well as competitive compensation/benefits such as actuarial exam raises and flexible work arrangements. Click here to apply through Handshake

SimBioSys (http://www.simbiosys.com) is seeking an energetic, responsible intern with acute attention to detail to join our organization for Summer 2021 – Winter 2021. In this position, you will be applying software engineering principles to build a functional tool for use by physicians. During this internship you will work within a diverse team (scientists, developers, physicians) to design and maintain TumorScope, an application stack that helps in the treatment of cancer patients. You will learn how to implement web services, scale code on AWS, and develop front-end applications. Fostering your agile development, communication, test-driven design, and programming skills, this internship will provide you real-world experience solving problems, while allowing you to contribute to the fight against cancer. Sound interesting? Even if you meet only 80% of the requirements, apply now! Send your resume to: jrp@simbiosys.com Intern Duties and Responsibilities Design, develop and maintain software using modern programming techniques Interact with scientists and physicians to create user stories Assess performance, stability, and security up and down the application stack Assess human usability factors of the front-end application Requirements and Qualifications Junior-level or higher Bachelor’s candidate in Computer Science, Computer Engineering, or a related degree Strong computer skills Proficient in Python, JavaScript, HTML, and CSS Excellent written and verbal communication skills Ability to work independently and as part of a team to meet project goals Desired Qualifications Experience with a Python web framework like Flask, Django, Bottle, etc. Experience designing RESTful APIs Experience with Design-Build-Test software development processes Experience with AWS, DevOps, or Infrastructure as Code Experience in designing and implementing web applications with a front-end framework such as ReactJS, Angular, Vue.js, etc. Experience with databases, both relational and NoSQL About SimBioSys One in eight women will be diagnosed with breast cancer, requiring some combination of chemotherapy, immunotherapy, radiation, or surgery. Half of second opinions will differ radically from the first. This is not the fault of the clinicians; rather, limitations in current diagnostics often result in ineffective, inaccurate prognoses. SimBioSys is developing technology for exploring the way a patient’s individual tumor will respond to therapy (aka a TumorScope). Integrating the gamut of health data available for a patient, the novel capabilities within the TumorScope not only provide predictions for treatment outcomes, they provide the reason why a treatment will succeed or fail. As a first target, SimBioSys is calibrating the TumorScope for the war on breast cancer. Empowered with an understanding of how a patient’s tumor responds to different therapies with quantitative measures of the likelihood of success doctors can de-risk their decision making, standardize their treatment approach, and provide confidence and certainty to their patients. SimBioSys, Inc. is an equal opportunity employer and all qualified applicants will receive consideration for employment. Sound interesting? Even if you meet only 80% of the requirements, apply now! Send your resume to: jrp@simbiosys.com

Machine Learning for Characterizing Crop Protection Formulation Landscape Project Goal The goals of this project are to develop and deploy machine learning applications for creating and visualizing crop protection formulation landscape using data from different internal (e.g. PTIP, GPS) and external sources (e.g. safety datasheets (SDSs), technical datasheets from vendors, and other relevant open source data). Formulation landscape here means a representation of all formulations together for identifying hidden patterns to address various questions that are critical to formulation development e.g., Which formulations are similar to a given formulation? How does a new formulation relate to existing products? What levels of built-in adjuvant or other ingredients are typical? What are most commonly used co-formulants? What are the relationships between ingredients? What approaches have been applied in the past to address specific formulations challenges? How did formulations perform with a specific surfactant? What is the influence of molecular properties of active ingredients, adjuvants, surfactants, and co-formulants on performance of final formulation? Can we predict performance of compositions dependent on the properties of their components (adjuvants etc.)? Background and Motivation The Crop Protection Business is working aggressively to double EBITDA over the next 5 years, in part by achieving >$1.8B in revenue growth from new product launches.  Achieving this goal requires that we become what Jim Collins has called a “product-launching machine.”  Formulations R&D plays a crucial role within this machine by determining the product composition that meets customer requirements.  Formulated products are complicated mixtures that are challenging to design due to the large number of input variables (e.g. type and concentration of emulsifiers, wetters, dispersants, rheology modifiers, solvents, and adjuvants along with processing parameters) as well as the complexity of product requirements (dilution behavior, storage stability, compatibility with other tank-mixed products, and regulatory constraints).  The multi-dimensionality of formulations makes it challenging to see relationships between different formulations and correlations between composition and performance.  The project proposed herein aims to make these relationships and trends visible, thus enhancing our ability to learn from the past, leverage learnings between different projects, identify IP opportunities, and ultimately develop better products faster. Project Description Data from internal and external sources (e.g. PTIP, GPS, internal reports, safety datasheets, technical datasheets) will be collected, cleaned, and organized for machine learning efforts Machine learning methods and applications will be developed to characterize and visualize current formulation landscape to address the questions mentioned in Project Goals section above. Deliverable(s) and Impact: What will be delivered at the end of the project (in order of priority): Datasets for creating formulation landscape and relating the trade names to actual chemistry A clear understanding of historical information through data analysis and visualization An application to visualize formulation landscape Machine learning methods to address the questions mentioned in Project Goals section above What is the impact that the deliverables are going to create:  Formulation design Generate a clear view on what’s been used in the past Select the commonly-used ingredients to streamline development and production Try new ingredients through a clear view of formulation landscape Troubleshooting formulation issues Establish “formulation similarity index” to help scientists get the most out of their data (i.e., if a customer complains about formulation X to our product support group (PTEC), then PTEC can look in their database to see all the previously reported issues around formulation X.  Maybe they find nothing or perhaps there’s a formulation Y that’s almost identical to formulation X and we have tons of data on Y). Formulation risk assessment It would be helpful to put risk assessment in the context of previous formulations to see how similar/different it is when a new formulation is evaluated.  If it’s quite different, the risk of performance issues is higher and requires a higher level of testing. Desired Skill Set Python programming Machine/Deep Learning Data mining Web application deployment Data visualization Basic knowledge of chemistry This role is open to current students of the University of Illinois – Champaign/Urbana campus and is eligible for up to 40 hours of work time per week. Semester: Summer 2020 (May 18 – August 6, 2020) 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. To apply, email your resume to Lisa Brooks, Lisa.DunganBrooks@corteva.com. Please reference “Machine Learning for Characterizing Crop Protection Formulation Landscape Internship” in the Subject line and within the email body. Your resume will be forwarded to the proper hiring team for consideration.

Corteva Agriscience™ has an exciting internship opportunity at UIUC campus during Summer 2020. The successful candidate will work with leading research and development scientists to develop and apply state of the art statistical and machine learning methods in the area of bioengineering and bioprocessing. Project: Impact of data partition on fermentation optimization Required Qualifications Currently enrolled in a graduate program in quantitative sciences such as Statistics, Computer Science, Bioinformatics or a related field. Experience with statistical inference and prediction modeling. Knowledge of descriptive statistics and experimental data analysis, particularly analysis of variance, mixed models and non-linear regression. Programming experience in R or Python, preferably in high-power computing environment. Effective written and verbal communication skills. Candidate should demonstrate ability to successfully collaborate with Scientists. Attention to detail is a critical skill for the internship. Candidate should be able to develop accurate software code and written reports. Critical thinking and strong problem-solving skills are a plus. This role is open to current students of the University of Illinois – Champaign/Urbana campus and is eligible for up to 40 hours of work time per week. Semester: Summer 2020 (May 18 – August 6, 2020) 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. To apply, email your resume to Lisa Brooks, Lisa.DunganBrooks@corteva.com. Please reference “Impact of data partition on fermentation optimization Internship” in the Subject line and within the email body. Your resume will be forwarded to the proper hiring team for consideration.

Deep Learning Accelerated Manifold Learning for Reactive On-line Environment Classification Project Goal A user interface and machine learning method enabling real-time, deep-learning-accelerated manifold exploration and learning to compress GxExM datasets into environment classes. Background and Motivation Genetics by environment by management datasets are being collected to develop environment and mega environment classifications for breeding program optimization. These datasets consist of highly multivariate and colinear variables. Optimal and actionable segmentations of environment are needed to provide breeding teams with environment targets for selections and to compare the rank genetic materials in muti-location environmental trials. These activities will occur within and across breeding evaluation zones and continental business structures. Furthermore, the genetic material available will change yearly and the problem differs significantly by crop. The dynamic factors of spatiotemporal environment and genetic variability will require a dynamic capability to efficiently compress and solve optimizations across the interacting objectives of environment class and product concept. Static envirotyping approaches will be effective only for static genotype x environment realities. Project Description The candidate will use GxExM datasets for NA corn breeding locations since 2012 and apply deep learning approximations of multiple manifold learning techniques to compress the dataset into meaningful latent spaces. The candidate will develop a live exploratory data analysis tool powered by these methods to enable rapid visualization and conceptualization of envirotypes and their match to product categories. This will be used to drive semi-supervised classification of environments. A further goal will be to explore the stochastic representation of environment possibilities and techniques to match the probabilistic outcomes to geospatial product placement. Deliverable(s) and Impact: An exploratory data analysis UI backed by a deep learning accelerated architecture that will enable advanced breeding decision makers to explore environment data spaces. The tool and analysis will be used by breeders and discovery breeders to evaluate target populations of environment for EZ and mega-environment applications. The tool could enable exploratory TPE targeting, optimization of GxExM, and development of environmental classifications in an on-line and dynamic system. Desired Skill Set Strong knowledge in deep learning, statistics, manifold learning, or machine learning Python, R, docker, linux Dash, Shiny, or other hardware accelerated multivariate data visualization systems Geospatial data manipulation and analysis a plus This role is open to current students of the University of Illinois – Champaign/Urbana campus and is eligible for up to 40 hours of work time per week. Semester: Summer 2020 (May 18 – August 6, 2020) 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. To apply, email your resume to Lisa Brooks, Lisa.DunganBrooks@corteva.com. Please reference “Deep Learning Accelerated Manifold Learning for Reactive On-line Environment Classification Internship” in the Subject line and within the email body. Your resume will be forwarded to the proper hiring team for consideration.

Corteva Agriscience™ seeks students for full-time internships within our global Data Science and Informatics group working on rapid imaging solution development for digital agriculture at our University of Illinois at Urbana-Champaign offices. We are aggressively building Big Data and Predictive Analytics capabilities to deliver improved services to our customers. Our interns will work on key problems related to delivery of imaging technology applications for internal phenotyping assays. The intern should be familiar with software development tools and practices on Linux/UNIX environments. Intern will work with imaging scientists, machine learning engineers and clients to provide customer value. What you’ll do: Design and implement web-enabled customer-facing frontend and backend software development for clients to consume imaging-related assay outputs. You will utilize front-end web development tools and frameworks such as React, JavaScript, Bootstrap, VUE.js, etc. to create easy-to-use user experiences. Develop appropriate backend infrastructure with tools and languages such as Python, SQLAlchemy, Pandas, OpenCV, SciKit-image, NodeJS. Create interfaces against production data repositories to consume imagery and related metadata. Enable scientific software development by following best practices and employing basic project scaffolding approaches (semantic versioning, security, infrastructure-as-code, etc.). Meet and work closely with clients to get requirements and feedback on solutions. Education and experience: Currently enrolled in an undergraduate education program pursuing (or planning to pursue) a B.S. in Computer Science, Software Engineering or related scientific discipline. Experience in C/C++ and Python programming with the ability to quickly create prototype solutions on Unix / Linux platforms. Development with front-end and back-end web technologies e.g. service-oriented architectures, React, AngularJS, Flask, FastAPI, MongoDB, REST APIs, JSON, HTML, JavaScript, CSS, NPM, grunt, gulp, basic project scaffolding. Strong knowledge of Linux with the ability to be able to build applications via the command line, debug issues, update software on Linux servers & perform system administration tasks. Proficiency with bash scripting and CLI tools (awk, sed, find, xargs, wc, head). Experience with Docker, application containerization, deployment and orchestration. Interest in learning new technologies, programming techniques, languages, and operating systems is a must. Excellent interpersonal skills and a can-do attitude with the ability to thrive in a fast-paced dynamic environment. Excellent analytical and problem-solving skills with the ability to work as part of a global team and, at times, independently while appropriately prioritizing tasks. Strong verbal and written communication skills in English are required. This role is open to current students of the University of Illinois – Champaign/Urbana campus and is eligible for up to 40 hours of work time per week. Semester: Summer 2020 (May 18 – August 6, 2020) Rapid Imaging Solutions Developer Project Goal Working closely with project stakeholders, enable client-facing web interfaces and related solutions to display and consume image data for imaging related assays. Create easily configurable and user-friendly interfaces with code reusability in mind so that solutions can be extended to other projects with minimal effort. Project Description The intern will be doing last-mile development. This includes UI mockups and development, integration or development effort required for value generation for the client. Deliverable(s) and Impact: What will be delivered at the end of the project: Last-mile development solutions. The interface, integration and development effort required for value generation for the client. Interfaces to consume imagery data based on common infrastructure. What is the impact that the deliverables are going to create: Enable digital transformation via enablement of AI/Computer Vision solutions to end clients. Digital Transformation. 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. To apply, email your resume to Lisa Brooks, Lisa.DunganBrooks@corteva.com. Please reference “Rapid Imaging Solutions Developer Internship” in the Subject line and within the email body. Your resume will be forwarded to the proper hiring team for consideration.

Training and evaluation of multi-region remote sensing yield prediction models with additional remote sensing/large scale features Project Goal To determine the impact of implementing a multi-region yield prediction strategy on our ability to predict yield across varied environments. To determine the impact of adding more satellite features into the current yield prediction model Background and Motivation We currently utilize a single yield prediction model for predictions across the continental United States which has been trained on > 1 million corn, and >600K soybean fields. We have shown that we have reached a point in which adding addition training data will not significantly increase the accuracy of our models. The goal of this project will be to try several approaches to determine if we can improve the model’s accuracy by 1. creating new models based on state, CRM, evaluation zone, etc., or 2. adding additional features, satellite measured soil moisture, soil attributes, etc. Project Description Project Outline: Identify the zones to be tested and features to be added Extract the additional features and group data into the determined zones to create new training data sets Train/evaluate machine learning model Evaluate the performance of the model compared to the current approach Assemble and communicate the findings to the group This project will require knowledge of geospatial data structures, data manipulation tools (python, pandas, etc), remote sensing basics, machine learning, and data visualization. Deliverable(s) and Impact: What will be delivered at the end of the project: Clear understanding of the impact of a multi-zonal yield prediction strategy and additional features on accuracy. What is the impact that the deliverables are going to create: We hope to increase the accuracy of our remote sensing yield prediction models which can then be folded into current and future products which depend on this ability Desired Skill Set Knowledge of geospatial data structures, data manipulation tools (python, pandas, etc.), remote sensing basics, machine learning, and data visualization Machine learning experience does not have to be extensive, but candidate should be familiar with concepts Candidate should be familiar with: Python/R, pandas, geopandas, xgboost, multivariate linear regression, geospatial data visualization, scientific plotting, basic statistics This role is open to current students of the University of Illinois – Champaign/Urbana campus and is eligible for up to 40 hours of work time per week. Semester: Summer 2020 (May 18 – August 6, 2020) 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. To apply, email your resume to Lisa Brooks: Lisa.DunganBrooks@corteva.com. Please reference “Training and evaluation of multi-region remote sensing yield prediction models with additional remote sensing/large scale features Internship” in the Subject line and within the email body. Your resume will be forwarded to the proper hiring team for consideration.

Insect Resistance Modeling Project Goal Continue ongoing work in insect resistance, document results internally, and finalize publication for insect resistance. Background and Motivation The onset of insect resistance to insecticide products or insecticidal traits often renders the product concept useless when it occurs. It takes years to build up resistance that often comes from repeated and overuse of the pesticide or trait. The onset of insect resistance must be numerical since one is dealing with potential future behavior, along with estimates of future market share (for new products), conventional pesticide use, and use of Bt crops. Insects are known to develop resistance to conventional pesticides, and often various management practices are used to delay resistance onset. Modeling the impact of various management practices on resistance development provides the registrant details on how to control the pest and manage the onset of resistance such that the product concept can be fully realized. Ideally, a product concept should last at least 10 years. Once resistance is fully developed, the pesticide product concept is ineffective and over. Thus, understanding ways which can delay the onset of resistance helps when making recommendations for the company that maximizes profit and extends the product lifespan. Project Description This project uses modeling approaches (Python) to generate tools that describe insect resistance predictions and various management practices/scenarios that can be used to influence resistance behavior. Deliverable(s) and Impact Much of this work has been generated but now requires summarization and extensions. The intern will take and expand existing technology and document via internal and external publication. This work and generation of these tools propels Corteva agriscience to the forefront of industrial knowledge in the area of resistance and offers the ability to set the future direction for existing and future product concepts (with product development managers in insect control) to maximize profit and product longevity. Desired Skill Set MS/PhD in Computer Science/Applied Math/Entomology This role is open to current students of the University of Illinois – Champaign/Urbana campus and is eligible for up to 40 hours of work time per week. Semester: Summer 2020 (May 18 – August 6, 2020) 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. To apply, email your resume to Lisa Brooks: Lisa.DunganBrooks@corteva.com. Please reference “Insect Resistance Modeling Internship” in the Subject line and within the email body. Your resume will be forwarded to the proper hiring team for consideration.

Spray Automatization Nucleation in Two Phase Holes Project Goal Explore the nucleation of holes that form in liquid sheets that initiate sheet breakup when 2-phase systems are sprayed through agricultural flat fan and air induction nozzles. Background and Motivation This is an extension of prior work that focuses on the mechanistic modeling of the breakup of 2-phase systems when delivered using agricultural nozzles. The development and extension of mathematical models from first principles regarding the breakup of liquid sheets into droplets has been proposed almost 50 years ago but only recently has theoretical work been undertaken to understand this phenomenon. A two-liquid phase (oil-in-water) continuous sheet of fluid emanating from a spray nozzle breaks into drops is first initiated by forming holes within the sheet. Corteva has various models describing hole grow (once formed) and collision to form ligaments, where the ligaments break into droplets via the well-known Rayleigh-Plateau stability theory. What is lacking is a hole perforation model, and thus model refinement is required for the development of a nucleation model that accounts for hole formation within a continuous sheet of fluid that begins this process (which is largely unknown today). Use of analytical/math techniques to quantify experimental observations aligns specifically with the Stats/Math group of Corteva agriscience. Understanding the ultimate drop size distribution that a liquid sheet succumbs to is a capability development for the company and industry in modeling spray atomization. Once nucleation of holes within the sheet is known and adequately addressed, Corteva agriscience has all the fundamental modeling pieces in place for developing the next generation of low drift two-phase formulations to achieve low drift potential [i.e., nucleation of holes (what will be addressed by this proposal), hole growth and collision to form ligaments, followed by ligament breakup into drops]. A strong understanding of fluid dynamics is required. Project Description This is a physical modeling approach via fluid mechanics which begins with an oil-in-water system where the oil phase (e.g., oil drop) is large enough to touch both interfaces of a liquid sheet emanating from a spray nozzle (it is assumed this is what must happen to initiate a hole in a sheet that ultimately leads to sheet rupture and is supported by video observations) which is assumed to lead to a hole forming within a sheet. Holes that form ultimately leads to sheet rupture. The modeling tool(s) to be developed can be a first principles high level programming language (Python) which will be used to supplement the other tools that have been developed once holes are formed. Approaches beginning with hole nucleation, coupled with hole growth, ligament formation, and ligament breakup in drops can coupled to generate a comprehensive tool created from this work. Deliverable(s) and Impact A modeling tool that can be used to recover experimental observations of the breakup of two-phase sheets from agricultural nozzles beginning with nucleation of holes that form in these fluid sheets to ultimately obtain the droplet size we are after (e.g., reduction of drops < 150 um) is the final piece that is lacking for sheet breakup of two-phase systems. This project will focus on the nucleation of holes (where, why and how) Formulation and Application team members will benefit (Brandon Downer, Paul Larsen, Abrin Schmucker) who routinely randomly design and test different formulations and often take the “best” from limited experiments/observations. However, a modeling tool for determining the nucleation of holes within a properly designed 2-phase formulation is sought such that optimal spray characteristics are obtained [e.g., ultimately reduce the driftable fine fraction when applied by standard application methods (e.g., ground and aerial spray) while keeping the larger drops of similar size]. This is a carry-over project from summer 2019 where the student (Jason Turner) took up where the IIT Madras masters student (Nikhil Rajan) left off, refining the python code for hole formation, expansion and breakup into droplets, with refinement on why holes are initiated in two-phase systems (the last unknown in modeling this behavior) Desired Skill Set Fluid mechanics knowledge, Engineering (Mechanical, Civil, Chemical), Theoretical and Applied Mechanics, Applied Math, Applied Physics. This is a carry-over project for Fall 2019 from full time summer work by Jason Turner (MS UIUC Aerospace Engineering) This role is open to current students of the University of Illinois – Champaign/Urbana campus and is eligible for up to 40 hours of work time per week. Semester: Summer 2020 (May 18 – August 6, 2020) 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. To apply, email your resume to Lisa Brooks: Lisa.DunganBrooks@corteva.com. Please reference “Spray Automatization Nucleation in Two Phase Holes Internship” in the Subject line and within the email body. Your resume will be forwarded to the proper hiring team for consideration.