Doctoral student in Electrical Engineering with focus on Federated Learning on IoT - Hiring in process/Finished, not possible to apply

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/

The graduate school within WASP is dedicated to provide the skills needed to analyze, develop, and contribute to the interdisciplinary area of artificial intelligence, auto-nomous systems and software. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD-students, researchers and industry. Read more: https://wasp-sweden.org/graduate-school/

Description of the workplace

The Department of Electrical and Information Technology conducts high-quality research in a variety of areas in electrical engineering and computer science, ranging from pure theoretical research to applied projects in close collaboration with industry. It is at the forefront of international research in most of its areas. The Department of Electrical and Information Technology consists of about 100 active researchers, focusing on several research domains, including Secure and Networked Systems, Communications Engineering, Integrated Electronic Systems, Electromagnetics and Nanoelectronics, as well as Intelligent Systems and Machine Learning.

Subject description

Machine learning and artificial intelligence have attracted a lot of attention over the past few decades. Machine learning algorithms have been considered in many application domains, including Internet of Things (IoT) systems. The adoption of machine learning in IoT systems creates several new opportunities, e.g., detection of health abnormalities using wearable devices, but also involves several major challenges, e.g., complexity of federated and decentralized machine learning algorithms for IoT systems and privacy concerns related to personal data and machine learning.

Work duties

The main duties of doctoral students are to devote themselves to their research studies which includes participating in research projects and third cycle courses. The work duties will also include teaching and other departmental duties (no more than 20%).

The Doctoral student will work within the area of federated machine learning for IoT systems to tackle one of the main challenges in the machine learning domain. The project is cross-disciplinary between the machine learning and IoT areas, e.g., edge federated learning on IoT devices. An important part of the student's work will be to develop the theoretical foundation of federated learning and new algorithms to address the challenges within the subject area of this position. The student will also be given the opportunity to validate these solutions with experiments and simulations.

Admission requirements 

A person meets the general admission requirements for third-cycle courses and study programmes if the applicant:

• has been awarded a second-cycle qualification, or
• has satisfied the requirements for courses comprising at least 240 credits of which at least 60 credits were awarded in the second cycle, or
• has acquired substantially equivalent knowledge in some other way in Sweden or abroad.

A person meets the specific admission requirements for third cycle studies in Electrical Engineering if the applicant:

• at least 60 second-cycle credits in subjects of relevance to electrical engineering, or
• a MSc in Engineering in biomedical engineering, computer science, electrical engineering, engineering mathematics, nanoengineering, engineering physics or information and communication engineering.

Additional requirements:

• Very good oral and written proficiency in English.
• Very good programming skills.

Assessment criteria

Selection for third-cycle studies is based on the student’s potential to profit from such studies. The assessment of potential is made primarily on the basis of academic results from the first and second cycle. Special attention is paid to the following:

1. Knowledge and skills relevant to the thesis project and the subject of study.
2. An assessment of ability to work independently and to formulate and tackle research problems.
3. Written and oral communication skills.
4. Other experience relevant to the third-cycle studies, e.g. professional experience.

Consideration will also be given to good collaborative skills, drive and independence, and how the applicant, through his or her experience and skills, is deemed to have the abilities necessary for successfully completing the third cycle programme.

We offer

Lund University is a public authority which means that employees get particular benefits, generous annual leave and an advantageous occupational pension scheme.  
Read more on the University website about being a Lund University employee Work at Lund University. 

Terms of employment 

Only those admitted to third cycle studies may be appointed to a doctoral studentship. Third cycle studies at LTH consist of full-time studies for 4 years. A doctoral studentship is a fixed-term employment of a maximum of 5 years (including 20% departmental duties). Doctoral studentships are regulated in the Higher Education Ordinance (1993:100), chapter 5, 1-7 §§.

How to apply

Applications shall be written in English and include a cover letter stating the reasons why you are interested in the position and in what way the research project corresponds to your interests and educational background. The application must also contain a CV, degree certificate or equivalent, and other documents you wish to be considered (grade transcripts, contact information for your references, letters of recommendation, etc.).

Welcome to apply!