PhD student in Electronics - Hiring in process/Finished, not possible to apply

PhD student in Electronics with a focus on Machine learning in energy-constrained embedded systems

Position description
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 machine part 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 typically means that the machine learning algorithms have to operate 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, analysing their energy requirements and limitations, as well contributing to the development of novel and enhanced approaches. With this, 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 have an own drive in the development of your research agenda, and to contribute to the publication and presentation of results. The research group, in turn, will provide supervision and assistance during your studies, supporting your research and career development.

Entry requirements
In order to be enrolled for doctoral studies at Mid Sweden University, applicants must have a second-cycle degree or have completed studies of at least 240 ECTS (or equivalent), of which at least 60 ECTS were awarded in the second cycle, or have equivalent qualifications. Moreover, this position requires at least 90 ECTS (or equivalent) in electrical engineering, electronic engineering, or computer engineering, or other closely-related subjects.

Selection criteria
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.

Employment: This post-graduate employment is intended to be equivalent to four years of full-time research studies and is to lead to a doctoral degree. The intended start of employment is March 2020, or based on agreement.

Place of work: Sundsvall

Salary: Paid by Mid Sweden University at the regulated salary rate for PhD students.

Information: Further information is available 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: www.miun.se/stc.

Application: The application must include a CV, transcripts from your prior degree programme(s), and a letter of motivation.

We welcome your application through our recruitment system by 12th of february 2020 at the latest.