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Do you know that the materials you use every day in computers, mobile devices, batteries, medical devices, and airplanes take 20-30 years to transition from research laboratories to real-world applications? This course aims to teach techniques and methods to decrease this lengthy process to 3-5 years. Learn high throughput techniques and AI integration to expedite the process. Explore methods like high throughput additive manufacturing, combinatorial thin film technology, and chemical flow synthesis, along with computational tools like Density Functional Theory and machine learning. Enhance your skills for faster materials discoveries, vital for both academic research and industry innovation, paving the way for a sustainable future.
General information
Throughout history, new materials have catalyzed technological advancements. Finding these materials has become more complex and expensive, leading to longer development cycles and higher investment risks. Promising new approaches are emerging to address these challenges and expedite material discovery. These methods integrate high-throughput experimentation with cutting-edge computational tools and artificial intelligence (AI) to model material properties, forecast their behavior under various conditions, and guide the experimental synthesis process.
This course is structured to offer students an in-depth understanding of innovative and accelerated methodologies for developing novel materials. You will collaborate on projects and laboratory work in group settings. Topics for projects span a range of materials, including magnetic materials, battery materials, structural materials, and various techniques, and can be tailored to match your background and interests. The course will include lectures and group discussions, with assessment based on report writing and a final project presentation.
Prerequisites
The course is open to students from various master’s programs, such as Materials Engineering, Materials Chemistry, Nanotechnology, Physics, Product Development, Production Engineering, etc. PhD students and alumni whose research or work profiles are related to materials in any way are highly encouraged to enroll in this course.
How to apply
Apply to all Tracks courses at universityadmissions.se/antagning.se.
At universityadmissions.se/antagning.se: Search for the course code TRA440.
Read more here.
For alumni, PhD-stduents and professionals the course selection follows a different process. See more information on Tracks web page.
Details
Teacher (s): Varun Chaudhary, Uta Klement, guest lecturers
Course dates: Study period 2
Credits: 7.5
Level: A
Course code: TRA440
Application deadline: