PhD in Operations Research and Data Science
Mission
The Risk Analytics and Optimization (RAO) laboratory at the Ecole polytechnique fédérale de Lausanne (EPFL) led by Prof. Daniel Kuhn invites applications for a PhD position in the area of operations research and data science. This role entails developing foundational theories, innovative models, and efficient, easily implementable algorithms with guaranteed performance for data-driven optimization with applications in operations research and data science. As part of a collaborative project with a prominent retail company, you will have access to extensive real-world data.
The primary focus of this position is to produce high-impact, cutting-edge research. The position is funded by the NCCR Automation, a large, multi-institutional initiative, including over 20 laboratories and 30 principal investigators. The NCCR Automation is dedicated to pioneering new approaches in the control of complex automated systems and implementing these solutions in real-world applications.
Main duties and responsibilities
• Develop innovative data-driven methodologies for applications in operations research and data science, including optimization and machine learning algorithms as well as novel statistical tools.
• Conduct rigorous mathematical analysis and computational experiments.
• Contribute to implementation of proposed methods in practice with our industry partner.
About the Research Group: The RAO laboratory specializes in optimization under uncertainty and machine learning. Our team members publish in leading journals and conferences in Optimization, Operations Research, Machine Learning and Control. Our PhD graduates and postdocs regularly secure competitive positions in academia and industry. For detailed information about publications, news as well as current and former team members please visit our website: https://www.epfl.ch/labs/rao/.
Profile
• Master’s degree in Mathematics, Statistics, Operations Research, Computer Science, Engineering, or a related field. Exceptional candidates with a bachelor’s degree may be considered for direct enrollment as doctoral students. (Candidates in their last year of master or bachelor studies are welcome to apply).
• Strong foundation in mathematics with solid implementation skills is highly preferred.
• Experience in continuous optimization, machine learning algorithms, and statistics is an asset.
• Effective communication skills.
We offer
Excellent working conditions and state-of-the-art infrastructure in a dynamic international environment at the forefront of research.
• Full-time fully funded PhD position at EPFL.
• A collaborative environment with leading experts in the field.
• Potential for real-world impact through direct collaboration with industry.
Informations
Application Process: Interested candidates should submit the following documents:
• Curriculum Vitae (CV): Include educational background, research experience, publications (if applicable), and contact information for at least three academic references.
• Academic Transcripts: Official records from all universities attended.
• Cover Letter: Briefly outline your motivation and how your background aligns with this position (maximum of two paragraphs).
• Research statement: Provide a summary of your research interests and background (maximum of two pages). In this statement, please highlight your proudest achievements and describe the most challenging task you have encountered, along with how you approached it.
Deadline: Review of applications will begin on Jan 15th, 2025. The position will remain open until filled.
Contact Information: Dr. Yifan Hu, yifan.hu@epfl.ch or Prof. Daniel Kuhn, daniel.kuhn@epfl.ch. Please do not send your application via the e-mail addresses. Only applications via the below link will be accepted.
If you are successful, you will need to enroll in one of the EPFL doctoral school programs. Please check this page for additional information. Please note that this is a separate application process necessary to be eligible to complete your PhD at EPFL.