PhD student, Electronics focused on Machine learning on embedded systems - Hiring in process/Finished, not possible to apply

Machine learning has in recent years gained in attraction as a tool for data analysis and data-driven modelling. In an industrial setting, for example, it can be used to evaluate the condition of a machine, or predict the failure of a component based on sensor data. Currently, however, the data analysis is in most cases performed offline, that means at a location different from that where the sensor data is acquired. Due to bandwidth limitations, security concerns, and real-time requirements, amongst others, it is desirable to bring the data analysis closer to the data source. This means that it is desirable to run the machine learning algorithms on embedded systems with constrained resources.

This PhD position focuses on machine learning for energy-constrained embedded systems, such as wireless sensors operated on batteries or energy harvesting sources. During your PhD studies, you will contribute to a better understanding in this domain, investigating current machine learning methods on low-power microcontrollers or alternative low-power computing architectures. You will be analysing energy requirements and limitations, as well contributing to the development of novel and enhanced approaches. As a result, you will contribute to an exciting young field of research, and acquire an attractive competence relevant for both academia and industry.

As a PhD student in the Autonomous Sensor Systems group, you will be a vital part of the research group, collaborating with other PhD students, post-docs and senior researchers. Your studies will consist of courses and own research work, developing your competences through theory and experiments. You are expected to bring forward own ideas in the development of your research agenda, and to contribute to the publication and presentation of results. The research group, in turn, will provide a forum for discussion, supervision and assistance during your studies, supporting your research and career development. You will be part of a leading group in the field of low-power sensing systems and be able to exploit the group’s large international network

Job description: Graduate studies including 100 % research.

Requirements and assessment: To be considered for a PhD position, the applicant must meet the entry requirements for admission to Mid Sweden University´s third-cycle programme. In order to meet the entry requirements the applicant must have a second-cycle degree, or have completed at least 240 ETCS credits of which at least 60 credits should be second-cycle courses. An additional requirement is that 90 credits of the 240 total must consist of courses in electrical engineering, electronic engineering, or computer engineering, or other closely-related subjects. The applicant is also eligible for consideration if s/he has acquired the corresponding knowledge in some other way.

Apart from the formal entry requirements, the selection will be based on previous experience (i.e., thesis work, projects, internships), relevance of previous educational programmes and courses, results of previous studies, and an interview with the applicant.

We see it as an advantage if you have previous experience with machine learning and embedded systems. For this position, you should also enjoy programming and have an experimental approach to solving problems. Personal qualities such as teamwork skills, initiative, and suitability for PhD studies will be weighted together with your knowledge, competences and experiences in the subject area of this position.

Terms of employment: The PhD position refers to employment corresponding to two years of fulltime studies which are estimated to lead up to a licentiate degree. On condition that additional funding is obtained, the employment may be extended to a PhD.

Place of employment: Sundsvall

Entry: The 2022-06-01 or according to agreement.

Salary: According to the Mid Sweden University salary scale for PhD students.

For further information please contact from Prof. Bengt Oelmann, bengt.oelmann@miun.se, phone, +46 (0)10 142 87 92, or from the Head of Department, Claes Mattsson, claes.mattsson@miun.se, phone, +46 (0)10 142 84 98. More information can also be found on the STC homepage: https://www.miun.se/en/Research/research-centers/stc/

The application should include a verified CV, copies of official transcripts and degree certificates, description of objectives according to the above as well as other documents the applicant would like to refer to.

Please welcome to apply through our recruitment tool no later than 2022-04-15.