University of Skövde | Postdoctoral Researcher Recruitment 2026 | Mechanical Engineering (Knowledge-Driven Optimization)
Organization
University of Skövde
Duration
2 Years
Fellowship Amount
Salary as per Swedish University collective agreement based on qualifications and experience.
Overview
About the Organization
The University of Skövde is a leading Swedish public university recognized for excellence in engineering, information technology, health sciences, and business research. The university maintains strong collaborations with industry and international research organizations while providing an innovative environment for interdisciplinary research and advanced technological development.
About the Position
The Department of Engineering is recruiting a Postdoctoral Researcher in Mechanical Engineering to join the Virtual Engineering (VE) Research Environment.
The successful candidate will contribute to research on knowledge-driven optimization, intelligent manufacturing systems, AI-assisted decision support, digital twins, simulation-based optimization, and sustainable production systems. The position also involves collaboration with industrial partners and participation in cutting-edge research projects related to smart manufacturing and Industry 4.0.
Job Responsibilities
The selected researcher will:
- Conduct research in knowledge-driven optimization.
- Develop AI-assisted decision support systems.
- Work on digital twins and intelligent manufacturing systems.
- Design optimization models for production and manufacturing.
- Perform simulation-based analysis and optimization.
- Collaborate with industrial partners and international researchers.
- Publish research in high-impact journals and conferences.
- Contribute to teaching or administrative duties (up to 20%).
- Participate in national and international research projects.
Eligibility Criteria
Applicants should possess:
- Ph.D. in Mechanical Engineering, Industrial Engineering, Information Technology, Manufacturing Engineering, or a closely related discipline.
- Doctoral degree completed within the last three years before the application deadline.
- Strong research background in simulation, optimization, or manufacturing systems.
- Excellent written and spoken English communication skills.
Preferred Qualifications
- Machine Learning and Artificial Intelligence.
- Digital Twins.
- Multi-objective optimization.
- Simulation-based optimization.
- Manufacturing systems engineering.
- Decision support systems.
- Research collaboration with industry.
- Scientific publications in reputed journals.
Employment Type
- Full-Time
- Postdoctoral Appointment
- Fixed-Term
Salary
- Salary is offered according to the collective agreement of Swedish universities and will be based on qualifications and experience.
Duration
- 2 Years
Why Apply?
- Work in one of Sweden's leading engineering research environments.
- Collaborate with internationally recognized researchers.
- Access advanced laboratories and industrial research projects.
- Gain experience in AI, digital twins, and smart manufacturing.
- Build an international academic research profile.
- Opportunity to publish high-impact research.
How to Apply
Interested candidates should submit their online application through the University of Skövde recruitment portal.
Applications should include:
- CV
- Cover Letter
- Degree Certificates
- Publication List
- Selected Scientific Publications
- Other supporting documents specified in the advertisement
Only complete applications submitted before the deadline will be considered.
Important Dates
- Application Deadline: 31 July 2026
- Employment Type: Fixed-term (2 Years)
- Workload: 100% Full-Time
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Financial Support
Fellowship Amount: Salary as per Swedish University collective agreement based on qualifications and experience.
Eligibility Criteria
Strong research experience in optimization, simulation, manufacturing systems, AI, or related areas.
Excellent English communication skills.
Important Dates
| 0 | Application Deadline: 31 July 2026 |
| 1 | Employment Type: Fixed-term (2 Years) |
| 2 | Workload: 100% Full-Time |
Streams
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