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Lund University was founded in 1666 and is repeatedly ranked among the world’s top 100 universities. The University has 40 000 students and more than 8 000 staff based in Lund, Helsingborg and Malmö. We are united in our efforts to understand, explain and improve our world and the human condition.

LTH forms the Faculty of Engineering at Lund University, with approximately 9 000 students. The research carried out at LTH is of a high international standard and we are continuously developing our teaching methods and adapting our courses to current needs.

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, autonomous 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/

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. However, enabling machine learning in the IoT domain also involves several major challenges inherent to the IoT systems, e.g., limited processing power.

Work duties
The Doctoral student will work within the area of machine learning for IoT systems. The project is cross-disciplinary between the machine learning and IoT areas, e.g., edge machine learning on IoT devices. An important part of the student’s work will be to develop the theoretical foundation of machine 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 in testbeds and simulations.

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%).

Admission requirements

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

  • 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 he or she has:

  • 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.
  • Good ability to mature with the research education and contribute to the research group.

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.

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 §§.

Instructions on 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.).

Lund University welcomes applicants with diverse backgrounds and experiences. We regard gender equality and diversity as a strength and an asset. We kindly decline all sales and marketing contacts.

To apply, please click the button “Login and apply”

Type of employmentTemporary position longer than 6 months
First day of employmentAccording to agreement
SalaryMonthly salary
Number of positions1
Working hours100
CityLund
CountySkåne län
CountrySweden
Reference numberPA2020/4043
ContactAmir Aminifar, Associate Senior Lecturer, amir.aminifar@eit.lth.se
Union representativeOFR/ST:Fackförbundet ST:s kansli, 046-222 93 62SACO:Saco-s-rådet vid Lunds universitet, 046-222 93 64SEKO: Seko Civil, 046-222 93 66
Published11.Dec.2020
Last application date10.Feb.2021 11:59 PM CET

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