Uppsala University is a comprehensive research-intensive university with a strong international standing. Our ultimate goal is to conduct education and research of the highest quality and relevance to make a long-term difference in society. Our most important assets are all the individuals whose curiosity and dedication make Uppsala University one of Sweden’s most exciting workplaces. Uppsala University has over 54,000 students, more than 7,500 employees and a turnover of around SEK 8 billion.
Electrification, digitalisation and health technology are among the largest areas for the future in the conversion to sustainable societies. The Department of Electrical Engineering conducts successful research and education in the areas – renewable energy sources, electric vehicles, industrial IoT, 5G communication and wireless sensor networks as well as smart electronic sensors and medical systems. The Department of Electrical Engineering is an international workplace with around 140 employees that all contribute to important technical energy and health challenges at the Ångström Laboratory.
The position will be placed at the Division of Solid-State Electronics, Department of Electrical Engineering.
Human body can be equipped with wearable electronic devices that generate, process and transmit data in real time to monitor health conditions and perform neurosurgical treatment for patients, for instance, suffering from Parkinson’s disease (PD), essential tremor, dystonia and other neurological conditions. One challenge to realizing such a system is to create a software toolkit for on-body secure, low-power processing and computing signals generated by sensors and actuators. The research assigned to the position has a focus on developing machine learning (ML) modules to process tactile and tremor signals generated by neuromorphic electronic skin (ne-skin). The ne-skin converts analogue tactile signals into electrical pulses, thus encoding the mechanical signals in spike timing. ML algorithms based on artificial neural networks (ANN) will be developed for processing data streams from the ne-skin. The ML algorithms mimic the computation function in the afferent nervous system prior to the brain. To support low-power and high-efficiency signal processing, ML based on spiking neural networks (SNN) will be developed to achieve local, low power signal processing on signals about tremor and tactility.
- Design ML algorithms for efficient processing tactile and tremor signals
- Apply and develop learning rules in SNNs
- Develop software based on the ML algorithms for processing tremor and tactile signals
- The applicant must hold a Swedish doctoral degree in physics, mathematics, computer science or electrical engineering, or a foreign degree that is deemed to be equivalent.
- The applicant must have experience in ML, neural network, AI and programming.
- Good oral and written proficiency in English.
We put an emphasis on your personal qualities such as good collaboration and communication skills with other researchers. You should be purposeful, structured and able to work effectively both individually and in groups.
About the employment
Temporary position, 12 months. Scope of employment 100 %. Starting date 2023-02-01 or as agreed. Placement: Uppsala
For further information about the position, please contact: docent Zhi-Bin Zhang, 018-471 3146, email@example.com.
Please submit your application by 30th of November 2022, UFV-PA 2022/3453
Are you considering moving to Sweden to work at Uppsala University? Find out more about what it´s like to work and live in Sweden.
Please do not send offers of recruitment or advertising services.
Submit your application through Uppsala University’s recruitment system.
Placement: Department of Electrical Engineering
Type of employment: Full time , Temporary position longer than 6 months
Pay: Individual salary
Number of positions: 1
Working hours: 100%
County: Uppsala län
Union representative: Seko Universitetsklubben firstname.lastname@example.org
Number of reference: UFV-PA 2022/3453
Last application date: 2022-11-30