The Laboratory for Intelligence in Design Engineering and Learning (IDEAL) in the Department of Mechanical and Process Engineering invites applications for one to two doctoral (Ph.D.) positions in the area of Machine Learning for Engineering Design under the guidance of Prof. Mark Fuge, the Chair of Artificial Intelligence in Engineering Design. The general area of the laboratory covers the study of Artificial Intelligence and Machine Learning in the context of Engineering Design problems in domains such as healthcare, power generation, aerospace, and robotics.
Project background This is a general search and is not specific to a particular project so we are prioritizing scientific excellence and fit to the laboratory over any particular topic area. General areas of current research interest for the lab include, but are not limited to:
- Generative Models
- Transfer Learning
- Formal Systems and Program Analysis
- Self-Supervised Learning
- Intersection of Mathematical Topology and Machine Learning
- Agentic/Multi-Agent coordination for Engineering Design
- Industrial Robotics and multi-robot coordination
- Development of Engineering Benchmarks or Evaluation Frameworks
- Other emerging areas at the intersection of ML/AI and Engineering Design
Job description As a Doctoral researcher, your responsibilities may include:
- Individual or collaborative work on research including writing publications and contributing toward individual or shared code bases
- Collaborating, where relevant, with partners from industry, academia, and national labs to translate real-world needs into scientific or technical questions
- Learning new skills in specialized graduate areas via courses and other educational offerings, or through self-learning
- Assisting with the teaching mission of the laboratory, which includes working with Masters and/or Bachelors thesis students as well as course assistance as needed
- Generally being a good lab citizen by assisting in various shared laboratory administrative tasks shared by the team
Profile The following are some characteristics that may align well with the position:
- We are open to diverse educational backgrounds of applicants which can include engineering, mathematics, computer science, physics or other degree backgrounds
- Students in the lab typically have interests in or expertise in one of the following areas: Machine Learning, Optimization, Simulation or Robotics
- Having a strong publication record across either competitive journals or Computer Science conference venues is a plus, but not a requirement, especially for those with practical experience or non-traditional career paths
- Experience with High-Performance Computing (HPC) environments or Software Engineering best practices is a plus, but not a requirement
- You should possess strong English language skills and the ability to work successfully and collaboratively on diverse, multinational teams
We encourage candidates to apply even if they do not think they possess every point above, particularly for candidates who may not have had access to certain development opportunities. We will work with any selected candidates to develop targeted competencies on a personalized basis.
We offer You can look forward to:
- World-class research infrastructure and excellent working conditions guided by our common understanding of a supportive shared work culture
- Opportunities to work with a diverse, motivated and multicultural team in a creative research environment
- An rich intellectual environment within the lab and the university that includes top scholars from across the globe
- Support for personalized professional development and mentoring with the ability to build a strong support network for your future career and be part of a laboratory with a strong track record of placing employees into competitive research positions, professorships and industrial R&D
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