Job Requisition ID #24WD80415Position OverviewIn order to hit global 1.5C climate targets and mitigate the impacts of climate change, it is now accepted that a combination of carbon removal and renewable energy strategies need to be rapidly researched and deployed at scaled.At Autodesk Research, we're building on our past work in generative design, dynamic system modeling and optimization, multi-disciplinary optimization, and machine learning to contribute to this effort. Our goal is to develop the next generation of high-quality optimization tools, metrics and frameworks that integrate multiple disciplines seamlessly into future sustainable design tools and processes.Specifically, data-driven, physics-based surrogate models can play a pivotal role in optimizing these systems. These models act as a bridge between complex computational models and the need for efficient decision-making in real or near-real time. Despite their potential, the field faces challenges, especially in balancing model accuracy and computational efficiency. Integrating machine learning algorithms with physics-based models requires deep understanding in both areas and is essential for creating accurate and efficient models, especially for large-scale or complex real-world physical systems.ResponsibilitiesWorking with a multi-disciplinary team to design and prototype state-of-the-art sustainability solutionsDeveloping and testing physics-based machine learning tools and surrogate modelsDeveloping optimization algorithms for designing real-world, multi-disciplinary use casesConducting tests and analysis for various design scenariosProduce research output and findings for potential publicationMinimum Qualifications A PhD or Master student in the field of mechanical or environmental engineering, climate science or a related discipline with a background in system-level optimization, multi-disciplinary optimization, machine learning, and data-driven methodologies.The Ideal Candidate Experience WithEnvironmental systems engineering (with a focus on carbon capture and storage and renewable energy)Simulation and optimization of physics-driven systemsMulti-disciplinary optimizationMachine learning and data-driven methods for optimizationSurrogate modeling for large-scale scenarios such as layout designPython programmingPreferred QualificationsDomain expertise in data-capture, modeling, simulation and optimization of large-scale carbon capture or renewable energy infrastructure (ie. Wind turbines or direct air capture facilities)Knowledge of or familiarity with PyTorch, and deep-learning methodsExperience in setting up 3D environments for simulationsLearn MoreAbout AutodeskWelcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.We take great pride in our culture here at Autodesk – our Culture Code is at the core of everything we do. Our values and ways of working help our people thrive and realize their potential, which leads to even better outcomes for our customers.When you’re an Autodesker, you can be your whole, authentic self and do meaningful work that helps build a better future for all. Ready to shape the world and your future? Join us!Salary transparencySalary is one part of Autodesk’s competitive compensation package. Offers are based on the candidate’s experience, educational level, and geographic location.Diversity & BelongingWe take pride in cultivating a culture of belonging and an equitable workplace where everyone can thrive. Learn more here: https://www.autodesk.com/company/diversity-and-belonging
View Original Job Posting