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Mattias O'Nils
11 months ago
This position has expired. You can find similar positions from the section below or browse our positions listing pages.
Postdoctoral Position in Industrial AI, Machine Learning, and Intelligent Systems at Mid Sweden University Mid Sweden University in Sweden
Degree Level
Postdoc
Field of study
Computer Science
Funding
This is a full-time (100%) postdoctoral position for 2 years starting from 2026-02-01 or as agreed. Salary is individual and set according to the fixed-term employment contract for post-doctoral appointments. No explicit mention of stipend amount or tuition coverage.
Deadline
Expired
Country
Sweden
University
Mid Sweden University
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Keywords
About this position
Mid Sweden University is inviting applications for a postdoctoral position in Industrial AI, focusing on machine learning and generative AI for industrial applications such as process optimization and predictive maintenance. The position is based in the Department of Computer and Electrical Engineering, where research is conducted at the Sensible Things that Communicate (STC) research center. The center specializes in sensor-based systems and services for the Internet of Things and artificial intelligence, collaborating closely with both national and international partners in academia and industry.
The successful candidate will engage in both experimental and theoretical research, including data collection, model development and verification, and generalization of solutions. The role requires active participation and collaboration within the research group, as well as communication and teamwork with external partners. The position is full-time (100%) for two years, starting from 2026-02-01 or as agreed, and is located in Sundsvall, Sweden.
Applicants must have a PhD in electronics, electrical engineering, computer engineering, or a related field, awarded within three years prior to the application deadline (exceptions for parental or sick leave apply). Previous academic experience in machine learning is essential. Candidates should be responsible, structured, and able to communicate fluently in English, both orally and in writing. The ability to independently plan and conduct research, openness to learning, and effective collaboration are important personal qualities.
Application documents must be submitted in Swedish or English and include a CV, copies of grades and diplomas, thesis, a personal letter describing your research interests and goals in machine learning, and any other relevant documents. The application deadline is 2025-12-31. For more information, contact Professor Mattias O'Nils or Head of Department Patrik Österberg.
Mid Sweden University offers a vibrant academic environment with two campuses in Sundsvall and Östersund, providing excellent opportunities for research and collaboration. The university values diversity, equal opportunities, and sustainability, contributing to a better society through internationally recognized research and education.
Funding details
This is a full-time (100%) postdoctoral position for 2 years starting from 2026-02-01 or as agreed. Salary is individual and set according to the fixed-term employment contract for post-doctoral appointments. No explicit mention of stipend amount or tuition coverage.
What's required
Applicants must hold a PhD or equivalent foreign degree in electronics, electrical engineering, computer engineering, or a related field, awarded no more than 3 years before the application deadline (exceptions for parental/sick leave). Previous academic experience in machine learning is required. Candidates must be able to communicate fluently in English, both orally and in writing, and demonstrate responsibility, collaboration skills, and the ability to independently plan and conduct research. Application documents must include a CV, copies of grades and diplomas, thesis, a personal letter describing research interests and goals in machine learning, and any other relevant documents.
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Professors
Professor and Head of Department
Sweden
Research Interests
Artificial Intelligence
Solid Mechanics
Deep Learning
Machine Learning
Fault Detection
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