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The Department of Electrical Engineering at Uppsala University conducts successful research and education in the areas of control and signal processing, machine learning, industrial IoT, 6G communication and wireless sensor networks as well as research and education within renewable energy, electric vehicles, Life Sciences, smart electronic sensors and medical systems. The Department of Electrical Engineering is an international workplace with around 160 employees that all contribute to important technical energy and health challenges at the Ångström Laboratory.
The position will be at the division of Signals and Systems, at the Department of Electrical Engineering.
Project description
Next generation of networked cyber-physical systems will support a number of application domains e.g. connected autonomous vehicular networks and collaborative robotics in smart factories. With the advent of massive machine-to-machine communication and IoT networks, huge volumes of data can be collected and processed with low latency through edge computing facilities. Distributed machine learning enables cross-device collaborative learning without exchanging raw data, ensuring privacy and reducing communication cost. Learning over wireless networks poses significant challenges due to limited communication bandwidth and channel variability, limited computational resources at the IoT devices, the heterogeneous nature of distributed data, and also randomly time-varying network topologies. In this project, the aim is to design and analyze fast, low-complexity communication-efficient distributed learning and optimization algorithms that are adaptive to the constraints posed by the underlying wireless networks. The nature of this project is multi-disciplinary, requiring tools from distributed optimization and control, wireless communications and networks, signal processing, statistical machine learning and random matrix theory. This project will advance the state-of-the-art in distributed learning over wireless networks, and also contribute to design guidelines for practical learning algorithms.
Duties
The main duties of this position include
Requirements
About the employment
The employment is a temporary position of 2 years according to central collective agreement. Full time position. Starting date as agreed. Placement: Uppsala
For further information about the position, please contact: Professor Subhrakanti Dey, e-post: subhrakanti.dey@angstrom.uu.se, tel: +46184717059.
Please submit your application by 31 of October, 2024 UFV-PA 2024/3112.
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Type of employment | Temporary position |
---|---|
Contract type | Full time |
Salary | Individual salary |
Number of positions | 1 |
Full-time equivalent | 100% |
City | Uppsala |
County | Uppsala län |
Country | Sweden |
Reference number | UFV-PA 2024/3112 |
Union representative |
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Published | 17.Sep.2024 |
Last application date | 31.Oct.2024 11:59 PM CET |