Postdoctoral position in Computer Science, federated machine learning - Befattningen är under tillsättning eller redan tillsatt, och det är inte längre möjligt att lämna in en ansökan

Umeå University, the Department of Computing Science, is seeking candidates for a postdoc position in resource-frugal federated learning for preserving security and privacy with focus on edge infrastructures. Deadline for application is February 20, 2020.

WASP
The position is funded by The Knut and Alice Wallenberg Foundation through The Wallenberg AI, Autonomous Systems and Software Program (WASP), Sweden’s largest ever individual research program, and a major national initiative for strategic basic research, education and faculty recruitment. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish society as well as industry. For more information about the research and other activities conducted within WASP please visit http://wasp-sweden.org/.

Project description and working tasks
The rapid increase of autonomous systems and applications are providing challenges in dealing with petabytes of data. These size and multidimensional features make the machine learning models larger and more complex. Classical centralized approaches to learning and inference fail to address the problems of resource and storage limitations, network bandwidth constraints, tail latency, energy-efficiency, and many more. This project focuses on design and implementation of resource-frugal and robust federated learning algorithms for preserving security and privacy, which are ideally suited for big-data and edge infrastructures.

This project leverages federated learning techniques for advancing state-of-the-art machine learning algorithms where data is geographically distributed and sensitive. Federated learning algorithms empower large-scale distributed nodes, i.e., mobile devices to train globally shared models without divulging the privacy of raw data. Sophisticated attackers leverage the limitations of data, model, target class(es), resources, the communication path for the deception of federated learning algorithms and also to violate security and privacy. By creating unique features (e.g., decentralized optimization, heterogeneity, cost-effective communication architecture, model agnostic learning and robustness) of federated learning algorithms, this project addresses the problems of limited resources, computation, communication, and energy-efficiency for preserving security and privacy. As a result, these features improve the safeguard of services and diagnosis ability of edge infrastructures.

In addition to own research, the selected candidate is expected to contribute towards the local research community by actively participating in the departmental and group research activities such as workshops, seminars, etc. These contributions can be within distributed systems research group, but collaboration with researchers in, e.g., machine learning, mathematical statistics, optimization, deep learning, trustworthy learning or artificial intelligence is expected. (For further information, see www.cloudresearch.org).

Terms of employment
The appointment is for two years full-time employment. Postdocs are typically offered the opportunity to gain teaching experience on suitable undergraduate courses. Expected starting date is May 1, 2020 or as otherwise agreed.

Qualifications
Applicants must have earned a PhD or a foreign degree that is deemed equivalent to a PhD in Artificial Intelligence, Machine Learning, Computer Science or a subject relevant for the position. The PhD degree should not be more than three years old by the application deadline, unless special circumstances exist.

Candidates are expected to have outstanding knowledge of machine learning techniques (preferably federated learning and trustworthy learning). Demonstrable knowledge of data privacy, data wrangling, deep learning, threats to machine learning, security and performance anomalies is a prerequisite. In particular, candidates should be well acquainted with modelling and implementing decentralized learning models to ensure security and privacy when data is geographically distributed and sensitive.

Since research is conducted in an international research environment, ability to work as well independently as to collaborate and contribute to teamwork are required. Very good command of the English language, both written and spoken, are key requirements.

We particularly invite female candidates to apply to ensure gender balance.

Application
A complete application should contain the following documents:

• Introductory letter including a 2-page statement of research interests relative to the above topics and a motivation of why your expertise is appropriate for the position.
• Curriculum Vitae (CV) including a complete list of scientific publications.
• Copies of degree certificates, including documentation of completed academic courses and obtained grades
• A copy of your PhD thesis and copies of (max 5) original research publications relevant to the above topics, numbered according to the publication list.
• Names and contact information for three persons willing to act as references.
• Any other information relevant for the position such as description of software development experience, or previous industry experience.

The application must be written in English or Swedish. Documents must be in Word or pdf format. Applications must be submitted electronically using the e-recruitment system of Umeå University, and be received no later than February 20, 2020. Reference number AN 2.2.1-1888-19.

Further information can be obtained from Assistant Professor Monowar Bhuyan, (email: monowar@cs.umu.se) and Professor Erik Elmroth (email: elmroth@cs.umu.se).

More about us:
The Department of Computing Science is a dynamic environment with over 120 employees representing more than twenty countries worldwide. We conduct education and research on a broad range of topics in Computing Science. The focus of the research in the Distributed Systems group is to design, develop, deploy distributed learning algorithms for (autonomous) resource and application management for different types of IoT, clouds and distributed systems.