Machine Learning for Materials Informatics
Instructor: Prof. Markus J Buehler, mbuehler [at] MIT.EDU (mbuehler[at]MIT[dot]EDU)
Jul 28 - Jul 31, 2025 (3.5 days)
Learn more here: https://professional.mit.edu/course-catalog/machine-learning-materials-informatics (link to sign-up there)
June 2–6, 2025, MIT Campus, Cambridge, MA
Dera iMechanica Community,
Below is information about a short course I will be offering at MIT this summer, in Live Virtual format: Machine Learning for Materials Informatics (Jul 29 - Aug 2, 2024). This is an exciting opportunity that will cover fundamentals and applications in the emerging space of AI/ML for engineering, featuring hands-on interactive code development in Jupyter notebooks. We'll do a deep dive into all critical tools from autoencoders to graph neural nets to multimodal LLMs and multi-agent modeling. Please reach out to me if you have any questions.
TL;DR: Back on MIT campus, in person, with technical lectures, group work, interactive labs and clinics, networking sessions, and participant talks! Materials design has endless applications in countless industries, and its impact is growing especially at the nexus of creating more sustainable, functional and efficient materials platforms. MIT Predictive Multiscale Materials Design short course will be held during the week of June 12-16, 2023, at MIT. Participants will earn an official MIT certificate.
In this course you will fully learn how to incorporate new materials informatics methods into your own material modeling, analysis and design processes in order to capitalize on recent AI breakthroughs, such as language models (e.g. GPT-3, BERT, LaMDA, etc.), DNA and protein models (e.g., AlphaFold), graph neural networks applied from molecular to macroscale structures, and a host of methods adapted for computer vision including diffusion models (as used in DALL-E 2 or Imagen), specifically for the analysis, design and modeling of materials. The course involves a mix of lectures, hands-on labs and clinics for an immersive experience. Participants will learn fundamentals and techniques to deploy machine learning in materials development and gain first-hand understanding of state-of-the art tools for varied applications ranging from data mining to inverse design. We will cover scales from the molecular to the continuum.
We at MIT, welcome you to join us this summer, in person, for a hands-on design-to-product Predictive Multiscale Materials Design course from June 13-17, 2022. All course registrants will receive an MIT certificate upon completion.
https://professional.mit.edu/course-catalog/predictive-multiscale-mater…
TL;DR, MIT Short Course, Live Virtual June 21-25, 2021; a hands-on bootcamp on multiscale mechanics, ML/AI, additive methods, biomaterials and bio-inspired materials and design. Registration closing soon - sign up now!
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Massachusetts Institute of Technology
Cambridge, MA
Faculty Positions in the Department of Civil and Environmental Engineering
The MIT Department of Civil and Environmental Engineering (CEE) invites applications for several tenure-track faculty positions to begin July 1, 2020 or on a mutually agreed date thereafter. Faculty appointments will be at the assistant or untenured associate professor level. In special cases, a senior faculty appointment may be possible.
Submit your abstract for the 2019 Society of Engineering Science meeting on the campus of Washington University in St. Louis (WashU), October 13-15, 2019, deadline April 30, 2019 Information about tracks and symposia: https://ses2019.wustl.edu/track-symposia/
Submit your abstract: https://ses2019.wustl.edu/track-symposia/abstracts/
We have one or more postdoc positions available, to be filled immediately, at MIT’s Laboratory for Atomistic and Molecular Mechanics, under the direction of Professor Markus Buehler. We are looking for postdocs in two broad areas, as described below.
Position #1: Materials science modeling
