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Mattias O'Nils
11 months ago
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PhD Position in Embedded AI-based IoT Systems at Mid Sweden University Mid Sweden University in Sweden
Degree Level
PhD
Field of study
Computer Science
Funding
The position is a full-time doctoral studentship for four years, with salary according to Mid Sweden University’s salary scale for doctoral students. The position is renewable according to Swedish Higher Education Ordinance. No specific stipend amount is mentioned.
Deadline
Expired
Country
Sweden
University
Mid Sweden University
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Keywords
About this position
Mid Sweden University is inviting applications for a PhD student position in Embedded AI-based IoT Systems within the Department of Computer and Electrical Engineering. The research is conducted at the Sensible Things that Communicate (STC) Research Centre and focuses on sensor-based systems and services for the Internet of Things (IoT) and artificial intelligence (AI). The AI-based IoT and Measurement Systems (AIM) group develops AI-driven methods to enhance measurement systems through indirect and resource-efficient sensing, targeting applications such as condition monitoring, predictive maintenance, and environmental analysis.
The doctoral project centers on integrating sensor fusion and machine learning on IoT nodes, aiming to develop intelligent, resource-efficient solutions for distributed systems. Research activities include theoretical modeling, experimental studies, and the development, implementation, and verification of AI algorithms close to sensors. The project is carried out in collaboration with national and international partners from both industry and academia, providing a dynamic and interdisciplinary research environment.
Applicants must have a second-cycle degree or at least 240 ECTS credits (with 60 at the second-cycle level), and at least 90 ECTS credits in Electronics or equivalent. Experience in the subject area, the ability to work independently and collaboratively, and fluency in spoken and written English are required. Application documents should include a certified CV, transcripts, degree certificates, degree project, a personal letter outlining research interests and goals, and any other relevant documents. Applications must be submitted in Swedish or English via the university's recruitment system by December 31, 2025.
The position is a full-time doctoral studentship for four years, with salary according to the university’s scale. The start date is April 1, 2025, or as agreed. The research is based in Sundsvall, Sweden, offering access to a vibrant academic community and high quality of life. For further information, contact Professor Mattias O'Nils or Patrik Österberg. The university values diversity and equal opportunities in its recruitment process.
Key research areas include Embedded AI, IoT Systems, Electronics, Sensor Fusion, Machine Learning, Condition Monitoring, Predictive Maintenance, Edge AI, and Measurement Systems.
Funding details
The position is a full-time doctoral studentship for four years, with salary according to Mid Sweden University’s salary scale for doctoral students. The position is renewable according to Swedish Higher Education Ordinance. No specific stipend amount is mentioned.
What's required
Applicants must have completed a second-cycle degree or at least 240 ECTS credits of higher education studies, with at least 60 ECTS credits at the second-cycle level, or have acquired substantially equivalent knowledge. Specific eligibility requires at least 90 ECTS credits in Electronics or equivalent. Applicants should have previous experience, education, and work related to the subject area, be able to independently plan and carry out research, work well in a group, and communicate fluently in spoken and written English. Application documents must be in Swedish or English and include a certified CV, transcripts, degree certificates, degree project, a personal letter describing research focus and goals, 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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