Student Employment & Internships

Job Description There are over 7 billion people on this planet. And by 2050, there will be 2 billion more… many moving into urban centers at an unprecedented rate. Making sure there is enough food, fiber and infrastructure for our rapidly growing world is what we’re all about at John Deere. And it’s why we’re investing in our people and our technology like never before! Here the world’s brightest minds are tackling the world’s biggest challenges. If you believe one person can make the world a better place, we’ll put you to work. RIGHT NOW. John Deere is an equal opportunity employer, including disabled & veterans.  Primary Location: United States (US) – Iowa  – Ames, IA Function: Data and Analytics (CA) Title: Part-Time Student – Data Science & Analytics – Ames, IA, Champaign, IL or Austin, TX – 118837 Onsite/Remote: Onsite Position Note: Relocation assistance is not provided. The Part-Time Student Program is primarily designed to augment the Company’s regular full-time staff and is for students who work in parallel to their school schedule YEAR-ROUND. Your Responsibilities As a Part-Time Student – Data Science and Analytics for JD Intelligent Solutions Group (ISG) located in Ames, IA, Champaign, IL or Austin, TX, you will… Utilize analytics techniques to clean, preprocess, explore, and analyze data sets and extract actionable insights for advanced automation & sensing projects. Collaborate in a fun, friendly, and fast-paced team environment across disciplines and with key stakeholders to deliver data-driven solutions for the business Present your findings in meetings and translate your results into reports and presentations This position is not available to students on immigration visas. What Skills You Need Must be registered as a full-time student at a U.S. accredited college/university. Graduation date of Spring 2027 or later Cumulative GPA of 2.8 or above Working knowledge of Python and other programming languages with generally high technical capability. School and/or work experience analyzing text and/or numerical data and building models. Good knowledge of one or more of the following: machine learning, Bayesian statistics, AI, signal processing, optimization, operations research, applied statistical analysis, algorithmic modeling techniques. Experience with complex data visualization methods Pursuing a Bachelor’s, Master’s, or PhD Degree in Agriculture, Computer Science, Data Analytics, Data Science, Engineering, Math, operations research, or Statistics; others may apply. What Makes You Stand Out Knowledge of transformers, CNNs, Large Language Models (LLMs) Knowledge of sensing technologies/hardware/IoT Domain knowledge in Crop Science, Agricultural Machinery, Soil Science, or GIS Knowledge of Pytorch, Databricks, ArcGIS, Git, Jupyter Notebooks, SQL Understanding of common machine learning algorithms and how to build predictive model At John Deere, you are empowered to create a career that will take you to where you want to go while working in an inclusive team environment. Here, you’ll enjoy the freedom to explore new projects, the support to think outside the box and the advanced tools and technology that foster innovation and achievement. Additionally, we offer a comprehensive reward package to help you get started on your new career path, including: Flexible work arrangements Highly competitive base pay Paid time off and holidy pay Savings & Retirement benefits (401K and Defined Benefit Pension) Healthcare benefits with a generous company contribution in the Health Savings Account Adoption assistance Employee Assistance Programs Tuition assistance Fitness subsidies and on-site gyms at specific Deere locations Charitable contribution match Employee Purchase Plan & numerous discount programs for personal use $15 – $40 hourly based on published rates for business function and education level. Follow this link to learn more about our Total Rewards Package https://bit.ly/3XCd8fL Must be 18 years of age or older to apply The information contained herein is not intended to be an exhaustive list of all responsibilities and qualifications required of individuals performing the job. The qualifications detailed in this job description are not considered the minimum requirements necessary to perform the job, but rather as guidelines. To apply, please use this link: Part-Time Student – Data Science & Analytics – Ames, IA, Champaign, IL or Austin, TX | Careers at John Deere

About Dow At Dow, we believe in putting people first and we’re passionate about delivering integrity, respect and safety to our customers, our employees and the planet. Our people are at the heart of our solutions. They reflect the communities we live in and the world where we do business. Their diversity is our strength. We’re a community of relentless problem solvers that offers the daily opportunity to contribute with your perspective, transform industries and shape the future. Our purpose is simple – to deliver a sustainable future for the world through science and collaboration. If you’re looking for a challenge and meaningful role, you’re in the right place. About the Role Dow is excited to pilot our Dow Career Experiences program with UIUC, adapting our proven internal gig model to create industry‑sponsored opportunities for graduate students to work on industrially relevant problems, right from their university campus.  As industrial employers increasingly seek work-ready graduates, our University Gig Program helps provide students more insight to the evolving skill demands of the workforce and the real-world challenges. A significant portion of Dow’s polyethylene product volume is used in film applications, particularly multilayer films. In these structures, layers of carefully selected properties and defined thicknesses are combined to achieve product properties beyond individual material potentials. Multilayer films are complex systems and selection of components and structures are often connected to deep subject matter expertise. In order to preserve knowledge and enable systematic evaluations numerous fundamental models have been developed over the years, many within Dow’s research organization. With the rise of machine learning, data driven methods became an additional tool in the toolbox for modeling these types of systems. The goal of this work is to conduct a thorough evaluation of data-driven and hybrid fundamental / machine learning methods for modeling the performance of polymeric multilayer films with a special focus on packaging applications and polyolefins. Dow will provide a dataset for benchmarking of the methods, and potentially contribute fundamental or semi-empirical relationships to complement machine learning models. Requirements PhD students currently enrolled in chemistry, chemical engineering, and material science engineering, graduating after 2026 Part‑time commitment (5-10 hours/week, 7-10 weeks) Short‑term, project‑based work, paired with Dow mentor(s) Paid opportunities (32/hr) Projects completed on the student’s university campus Responsibilities Hands‑on, experiential learning focused on industrially relevant skillsets Opportunity to make real‑world impact Deeper understanding of a career in industry Exposure to and expansion of a professional industry network Literature review of machine learning in polymeric multilayer performance modeling. Computational implementation of the methods in a designated environment. Integration of fundamental and semi-empirical methods. Evaluation of prediction accuracy, data requirements, and interpretability of the various workflows. How to Apply: Please send your resume/CV and a cover letter (optional) to Qiang Liu (qliu3@dow.com), Associate R&D Director of External Technology.

About Dow At Dow, we believe in putting people first and we’re passionate about delivering integrity, respect and safety to our customers, our employees and the planet. Our people are at the heart of our solutions. They reflect the communities we live in and the world where we do business. Their diversity is our strength. We’re a community of relentless problem solvers that offers the daily opportunity to contribute with your perspective, transform industries and shape the future. Our purpose is simple – to deliver a sustainable future for the world through science and collaboration. If you’re looking for a challenge and meaningful role, you’re in the right place. About the Role Dow is excited to pilot our Dow Career Experiences program with UIUC, adapting our proven internal gig model to create industry‑sponsored opportunities for graduate students to work on industrially relevant problems, right from their university campus.  As industrial employers increasingly seek work-ready graduates, our University Gig Program helps provide students more insight to the evolving skill demands of the workforce and the real-world challenges. This project will develop an optical chemical structure recognition (OCSR) workflow that automatically extracts chemical structure depictions from technical documents — journal articles, reports, and SDS (Safety Data Sheet) sheets — and converts them into machine-readable SMILES strings. Rather than rebuilding image-to-SMILES models (now state-of-the-art through systems such as DECIMER, SwinOCSR, MolScribe, and OCMR), the project will focus on assembling a robust end-to-end document pipeline: detecting and segmenting structure images on noisy, multi-column pages; evaluating and adapting existing OCSR models for specific document formats; and validating SMILES using standard cheminformatics checks. The outcome will enable conversion of unstructured chemical documents into structured molecular data suitable for search, modeling, and knowledge-graph integration. Requirements PhD students currently enrolled in chemistry, chemical engineering, and materials science engineering, graduating after 2026 Hands‑on, experiential learning focused on industrially relevant skillsets working with 15 to 20 year industry R and D professionals. Opportunity to make real‑world impact Deeper understanding of a career in industry Exposure to and expansion of a professional industry network Responsibilities A gig is a short‑term, part‑time, paid project where graduate students contribute to pre‑ or post‑competitive industrial challenges while gaining real‑world experience. Part‑time commitment (5-10 hours/week, 7-10 weeks) Short‑term, project‑based work, paired with Dow mentor(s) Paid opportunities (32/hr) Projects completed on the student’s university campus OCSR Pipeline + Benchmark Summary: A containerized workflow that ingests PDFs, extracts structure depictions, runs them through one or more OCSR engines, and outputs validated SMILES, along with a short report benchmarking performance and identifying limitations. Curated Evaluation Dataset: A representative dataset of document pages with labeled chemical structures and ground-truth SMILES, plus concise documentation for extending, re-running, and maintaining the pipeline. How to Apply: Please send your resume/CV and a cover letter (optional) to Qiang Liu (qliu3@dow.com), Associate R&D Director of External Technology.

About Dow At Dow, we believe in putting people first and we’re passionate about delivering integrity, respect and safety to our customers, our employees and the planet. Our people are at the heart of our solutions. They reflect the communities we live in and the world where we do business. Their diversity is our strength. We’re a community of relentless problem solvers that offers the daily opportunity to contribute with your perspective, transform industries and shape the future. Our purpose is simple – to deliver a sustainable future for the world through science and collaboration. If you’re looking for a challenge and meaningful role, you’re in the right place. About the Role Dow is excited to pilot our Dow Career Experiences program with UIUC, adapting our proven internal gig model to create industry‑sponsored opportunities for graduate students to work on industrially relevant problems, right from their university campus.  As industrial employers increasingly seek work-ready graduates, our University Gig Program helps provide students more insight to the evolving skill demands of the workforce and the real-world challenges. The global roofing market is rapidly evolving, driven by growing demands for energy efficiency, durability, and sustainable building practices. Commercial roofing membranes—especially TPO, EPDM, and PVC—are central to meeting these performance and environmental standards. Dow’s membrane technologies, including ENGAGE™ TR, INFUSE™ OBC, and NORDEL™ EPDM, are engineered for UV resistance, flexibility, fire performance, and long-term weatherability. EPDM-based membranes, in particular, offer exceptional weathering and ozone resistance, making them ideal for long-life, low-maintenance roofing systems. Dow’s TPO solutions are gaining momentum through partnerships with leaders like Carlisle Construction Materials, while its PVC membranes, enhanced with Elvaloy® KEE polymers, deliver superior chemical resistance for demanding commercial applications. Commercial roofing membranes are typically installed using one of three methods: fully adhered, mechanically fastened, or ballasted. In fully adhered systems, adhesives are applied to bond the membrane directly to the insulation or substrate, offering a smooth finish and strong wind uplift resistance. Mechanically fastened systems use screws and plates to secure the membrane at the seams, which are then heat-welded or sealed. Ballasted systems involve loose-laid membranes held in place by heavy materials like gravel or pavers. The choice of method depends on building design, climate, and performance requirements. Adhered systems are considered the most robust roofing method, offering long-term durability and resistance to high-speed winds (up to 130 mph for 30 years). Adhesives used in these systems include contact adhesives, which require application on both surfaces and bonding once tacky, and wet lay-in adhesives, which bond the membrane while the adhesive is still wet—though these are water-based and sensitive to temperature conditions. Spray adhesives simplify installation by allowing workers to stand while applying, while sprayless adhesives reduce labor and cure time but require careful canister maintenance. There’s a growing preference for low-VOC adhesives due to environmental regulations, although they may take longer to set compared to traditional VOC-based options. Overall, the market is trending away from mechanical fasteners and solvent-based adhesives toward cleaner, faster, and more sustainable bonding technologies. Can Dow participate in this market through functionalized polyolefin portfolio as a hot melt adhesive for pre applied adhesive or water-based primer/adhesive for these commercial membranes? Requirements PhD students currently enrolled in chemistry, chemical engineering, and material science engineering, graduating after 2026 Hands‑on, experiential learning focused on industrially relevant skillsets Opportunity to make real‑world impact Deeper understanding of a career in industry Exposure to and expansion of a professional industry network Responsibilities Part‑time commitment (5-10 hours/week, 7-10 weeks) Short‑term, project‑based work, paired with Dow mentor(s) Paid opportunities (32/hr) Projects completed on the student’s university campus Market and technology analysis of current state Design and model features that extends performance Document findings and present to university and industrial stakeholders Learn market, customer, and application knowledge in general roofing Learn key technologies, performance CTQs, and unmet needs in roofing Learn product and material value propositions and tradeoffs in roofing Work closely with mentor to design, and model new product features that will extend value proposition and benefits to customers Assisting in the design of experiments and test methods that simulate customer process conditions Ability to work independently with high levels of attention to detail Self-motivated, high initiative and excellent time management skills. Utilize scientific methods and data generation and interpretation methods Possess a high level of written, verbal and presentation communication skills. How to Apply: Please send your resume/CV and a cover letter (optional) to Qiang Liu (qliu3@dow.com), Associate R&D Director of External Technology.

About Rivian Rivian is on a mission to keep the world adventurous forever. This goes for the emissions-free Electric Adventure Vehicles we build, and the curious, courageous souls we seek to attract. As a company, we constantly challenge what’s possible, never simply accepting what has always been done. We reframe old problems, seek new solutions and operate comfortably in areas that are unknown. Our backgrounds are diverse, but our team shares a love of the outdoors and a desire to protect it for future generations. About the Role  Rivian internships are experiences optimized for student candidates. To be eligible, you must be an undergraduate or graduate student in an accredited program during the internship term with an expected graduation date between December 2026 through May 2028. Rivian’s Internship Program requires active student enrollment. Information regarding your expected degree completion date is collected solely to verify eligibility and determine your availability for future full-time opportunities. Rivian is an equal opportunity employer and does not use graduation dates to determine the age of applicants or as a basis for discriminatory hiring decisions.    If you are not pursuing a degree, please see our full time positions on our Rivian careers site.  Note that if your university has specific requirements for internship programs, it is your responsibility to fulfill those requirements.  In this role, you will assist the Electric Power Conversion (EPC) team in developing and validating motor control algorithms. This entails using Model-Based Design to simulate motor behavior, and then utilizing Python and SQL within our cloud data platform to analyze how these algorithms perform in the field. Responsibilities Develop motor control logic in Matlab/Simulink while simultaneously building Python/SQL scripts in Databricks. Create a unified workflow where simulation results are compared directly against fleet data. Run test cases on Hardware-in-the-Loop (HIL) benches to verify performance. Document and present root-cause findings to the EPC team. Work with data processing team to validate the performance of controls logic on fleet. Requirements Must be currently pursuing a PhD degree at the University of Illinois Urbana Champaign Actively pursuing a degree or one closely related in Electrical Engineering or Mechanical Engineering  Strong proficiency in Matlab/Simulink for control logic development. Core understanding of AC motor theory and 3-phase power electronics. Solid programming foundations in C and Python. Prior experience in the EV industry or with high-voltage laboratory equipment preferred. Knowledge of SQL and cloud-based data analysis preferred. Understanding of Functional Safety (ISO 26262) principles is a plus. Please apply here UIUC Research Park Intern – Motor Controls Development in Champaign, Illinois | Rivian