Postdoctoral Position in Distributed Learning and Optimization
Postdoctoral Position in Distributed Learning and Optimization
Uppsala University
Uppsala, Sweden
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
- Conducting theoretical and applied research in the field of distributed optimization and machine learning, in particular, to develop novel Federated and fully distributed machine learning/optimization algorithms, performance analysis of these algorithms on synthetic and real datasets
- Writing high quality technical research papers suitable for publication in highly regarded journals such as the IEEE Transactions, Automatica, Journal of Machine Learning Research, and also in top-tier international conferences in the area
- Taking part in research dissemination through departmental seminars, conference presentations nationally and internationally
- Teaching of undergraduate/postgraduate course amounting to no more than 20% of full-time workload
Requirements
- The applicant must hold a PhD degree or a foreign degree equivalent to a PhD degree in Electrical or Computer Engineering/Applied Mathematics with specialization in control and optimization, or statistical machine learning.
- The degree needs to be obtained by the time of the decision of employment. Those who have obtained a PhD degree three years prior to the application deadline are primarily considered for the employment. The starting point of the three-year frame period is the application deadline. Due to special circumstances, the degree may have been obtained earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc.
- Strong analytical and mathematical skills, and good working knowledge of several of the following topics: control theory, convex optimization, statistical machine learning, signal processing and wireless communications.
- Good programming skills in MATLAB or Python (essential) and C++ (desirable)
- Very good oral and written proficiency in English.
- Ability to carry out independent research as well as effectively collaborate with other team members
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