Postdoc position with focus on federated learning - Befattningen är under tillsättning eller redan tillsatt, och det är inte längre möjligt att lämna in en ansökan

The Department of Computing science seeks a postdoc who will work with trustworthy federated learning. The employment is full-time for two years with starting date by agreement.

Department of Computing science

The department characterized by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison. The department has been growing rapidly in recent years where focus on an inclusive and bottom-up driven environment are key elements in our sustainable growth. As part of this growth, we now look for a Postdoc in trustworthy federated learning. The workplace consists of a diverse group from different nationalities, background and fields. If you work as a postdoc you receive the benefits of support in career development, networking, administrative and technical support functions along with good employment conditions. See more information at:

https://www.umu.se/en/department-of-computing-science/ 

The research group in distributed system is internationally well-known and comprising more than 25 people of 10 different nationalities, and is currently recruiting up to 10 more researchers. The group’s research focuses on the (semi-)autonomous management of resources and applications to support the future digitized society. Target infrastructures range from single large-scale cloud datacenters to mobile edge clouds and include individual servers, clusters, or disaggregated systems, such as rack-scale systems. The research spans from basic to applied research, and even innovation via spin-off companies. Collaborations are performed with industries like Google, IBM, Intel, Red Hat, and Ericsson, as well as universities and institutes such as Princeton University, University of Massachusetts Amherst, Carnegie Mellon University, Princeton University, Lawrence Berkeley Lab, Nanyang Technical University in Singapore, Uppsala University, Lund University, Universidad Complutense de Madrid, Leeds University, Barcelona Supercomputer Center, TU Vienna, TU Delft, and many more. See www.cloudresearch.org for a presentation of the group as well as an overview of ongoing research projects and publication lists. For more information about the research group, please see: https://www.umu.se/en/research/groups/autonomous-distributed-systems-lab/ 

Project description and working tasks

The rapid increase of autonomous systems, connected devices, and distributed applications pose challenges in dealing with petabytes of data in diverse resource-constrained environments. Federated machine learning (FML) is collaborative learning to handle these problems without sharing data with centralised servers. However, several emerging threats target FML training, learning, and inference to fail or mislead models at early learning rounds. Attackers aim to break trustworthiness under different threat models, such as insiders-outsiders attacks, semi-honest or fully malicious participants, and attacks in training, learning, or inference phases. As a result, the learning models fail to provide acceptable performance. Therefore, this project aims to develop and implement trustworthy federated learning algorithms for limited and diverse non-iid (independent identically distributed) data under non-standard and adversarial settings, which are ideally suited for constraint environments and edge computing infrastructures. These goals can be achieved by inducing unique features in federated learning algorithms such as decentralised training, optimal device selection, secure learning and inference, fault-tolerance against failures and attacks, as well as resilient, fair and robust models. The ambition is to validate them in classical non-standard settings and apply them to solutions for constraint environments (e.g., Internet of Things (IIoT), healthcare systems, robotics) and edge infrastructures. Potentially, teaching up to a maximum of 20% can be included in the work tasks.

Wallenberg AI, Autonomous Systems and Software Program (WASP) is Sweden’s largest individual research program ever, a major national initiative for strategically motivated basic research, education and faculty recruitment. The program addresses research on artificial intelligence and autonomous systems acting in collaboration with humans, adapting to their environment through sensors, information and knowledge, and forming intelligent systems-of-systems. The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry. Read more: https://wasp-sweden.org/ 

Qualifications

A person who has been awarded a doctorate or a foreign qualification deemed to be the equivalent of a doctorate qualifies for employment as a postdoctoral fellow. Priority should be given to candidates who have completed their doctoral degree no more than three years before the closing date of the application. A candidate who has completed their degree prior to this may be considered if special circumstances exist. Special circumstances include absence due to illness, parental leave or clinical practice, appointments of trust in trade unions or similar circumstances. Postdoctoral fellows who are to teach or supervise must have taken relevant courses in teaching and learning in higher education.

Candidates are expected to have solid foundations in the theory and algorithms of project-related areas, such as federated machine learning, trustworthy systems, distributed systems, and excellent programming ability.

A strong command of both written and spoken English language is a key requirement. 

Besides creativity and a curious mind, important personal qualities include the ability to work independently as well as together with others either in a group or outside. You are also expected to have a willingness to develop yourself continuously to become a competent and independent researcher.

Knowledge and experience in federated learning algorithms, distributed algorithms, learning with small data, data-centric optimization, resilient or fault-tolerant learning, trustworthy learning, mathematical statistics, serverless systems, etc., is desirable.

Application

A full application should include:

• Personal letter that clarifies how your background meets the needs and expectations of this position,
• Curriculum vitae (CV) with publication list,
• Certified copy of doctoral degree certificate or documentation showing estimated date for doctoral degree,
• Certified copies of other diplomas, list of completed academic courses and grades,
• Copy of doctoral thesis and up to three relevant articles,
• Contact information for at least three reference persons,
• Other documents that the applicant wishes to claim.

The application must be written in English or Swedish. The application is made through our electronic recruitment system. Documents sent electronically must be in Word or PDF format. Log in to the system and apply via the button at the end of this page. The closing date is May 30, 2023.

Further details are provided by Asst. Prof. Monowar Bhuyan at monowar@cs.umu.se or Prof. Erik Elmroth at elmroth@cs.umu.se