Position Summary
Position:
Department:
Energy Science Center (ESC)
Institute:
ETH Zurich
Country:
Switzerland
Research Field:
Energy Systems, Computer Science, Applied Mathematics, machine learning
Posted Date:
Mar 23, 2026
Deadline:
open until filled
Offer Period:
1 Year
Offer Start Date:
as soon as available
Postdoc position on hybrid AI-optimization modelling
Project Description:
- The Energy Science Center (ESC) at ETH Zurich is an interdepartmental competence centre that facilitates energy research and teaching activities across research fields and departments, and promotes outreach and public visibility of energy research at ETH Zurich. We are seeking a highly qualified and motivated Postdoctoral Researcher to join our team working at the forefront of hybrid AI-optimization modelling. This position focuses on the design of AI-based surrogates of large-scale energy system models and will be integrated in the activities of the Nexus-e group with close links to the ETH AI Center and the Reliability and Risk Engineering Lab.
- Collaborate with other researchers to develop high-fidelity emulators for large-scale, energy system optimization models
- Lead the design and implementation of innovative methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs)
- Contribute to the strategic direction of research
- Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning
- Collaborate with industry partner to tackle challenges of practical relevance
- Collaborate with other researchers to develop high-fidelity emulators for large-scale, energy system optimization models
- Lead the design and implementation of innovative methods, which could include but are not limited to: Kriging surrogate, Polynomial Chaos Expansion (PCE), and Physics-Informed Neural Networks (PINNs)
- Contribute to the strategic direction of research
- Publish high-impact research in leading journals and present findings at international conferences on energy systems and machine learning
- Collaborate with industry partner to tackle challenges of practical relevance
Required Qualification:
Essential Qualification:
- A PhD or Doctoral degree in Energy Systems, Computer Science, Applied Mathematics, or a field focused on the intersection of Machine Learning and Optimization
- Proven expertise in surrogate modelling, specifically in designing neural architectures for emulating constrained optimization problems
- Advanced programming skills in Python, with deep experience in libraries such as PyTorch, Pyomo, and the broader scientific stack (Xarray, Pandas). Knowledge of MATLAB is desired but not a requirement
- A strong track record of publications in peer-reviewed journals involving advanced emulation or physics-aware AI
- Demonstrated ability to work effectively in interdisciplinary and multicultural research teams, bridging the gap between data science and energy engineering
- Proficiency in English is required
Preferred Qualification:
Not Available
Employment Conditions and Benefits:
Contract duration:
- The planned duration of the initial contract is one year, to be extended based on continued funding and successful performance.
Benefits:
- A young, dynamic and interdisciplinary research team addressing energy system challenges.
- An attractive research position in high-impact projects, at one of the world’s leading universities with a central workplace in Zurich.
- Competitive salaries paid according to ETH standards.
- Numerous benefits and flexible, family-friendly working conditions.
- Your career with impact: Become part of ETH Zurich, which not only supports your professional development, but also actively contributes to positive change in society
Application Process:
Documents Required:
- A letter of motivation (max one page)
- CV, including publications
- Two key outputs (these could be publications, software code, projects, etc)
How to apply:
- We look forward to receiving your online application with the following documents:
- Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.
About the Host Lab Group/Institution:
ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.
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Frequently Asked Questions:
Who can apply for a postdoctoral position?
Postdoctoral positions are open to candidates who have completed a PhD or will complete their doctoral degree before the start date. Eligibility requirements may vary depending on the funding scheme and host institution.
Can international candidates apply?
Yes. Most postdoctoral positions are open to international applicants. Visa sponsorship and work permit support are usually provided by the host institution, subject to local regulations.
Is prior postdoctoral experience required?
In most cases, prior postdoctoral experience is not mandatory. However, candidates with relevant research experience beyond the PhD may be preferred for certain positions.
What is the typical duration of a postdoctoral contract?
Postdoctoral contracts typically range from six months to three years, with the possibility of extension depending on funding availability and performance.
Are teaching responsibilities included?
Teaching responsibilities vary by institution and position. Some postdoctoral roles are research-only, while others may include limited teaching or supervision duties.
Can I apply for multiple postdoctoral positions?
Yes. Candidates are encouraged to apply for multiple suitable postdoctoral positions to increase their chances of selection.
Disclaimer:
This position is published for informational purposes. Please refer to the official application link for the most accurate and up-to-date details.