The Draper Scholars Program emphasizes empowering students in 14 key research areas to make the greatest impact. We encourage applicants to align their research with these topics.
Design Methodology
Draper has a very strong history of advanced electro-mechanical design of extremely high performance systems. We continue to explore state-of-the-art methods to advanced design in harsh environments.
We would be targeting PhD students for the development of novel approaches; and MS students for the application of existing approaches to specific problems of interest to Draper.
Technical Point of Contact
Research Interests
Novel Design Methodologies
Advanced methods of electro-mechanical design that challenge the current way of ‘doing design’ are sought. We’d like to apply innovative design methods that have potential to achieve extreme performance, ultra-low SWAP, and / or ruggedized operation in harsh environments (e.g. Long-duration Space, Hypersonics)
Example areas might include:
- Minute deformation in extreme thermal environments
- Novel, low-SWAP electro-mechanical sensors
- MEMS Stirling engine
Draper may share specifics of particular interests once the collaborative research topic are has been agree to.
Design Visualization and Trade Space Exploration
Visual Communication of a complex, multi-dimensional, inter-related design space has proven very effective in improving design decisions and accelerating the design process. Novel methods and approaches for visualization of the design space, as well as tools and methods for exploring the tradespace within designs are sought. Again, Draper may share specifics of particular interests once the collaborative research topic has agreed to.
Generative Design
Draper frequently works on complex engineering design problems that require tradeoffs and optimization across many criteria and constraints. Generative design algorithms can help engineers navigate these large trade spaces by automatically creating and optimizing diverse designs that can outperform manually designed systems across many variables (e.g. size, weight, power, materials, lifetime, etc.). We are seeking novel approaches to AI-driven generative design to increase performance, improve efficiency, or reduce time to manufacture in relevant engineering domains such as electro-mechanical systems, micro-electronics, and bio-engineering.
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