Two Post-Doctoral Positions in Sustainable Machine Learning

Lulea University
October 31, 2024
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Luleå University of Technology is in strong growth with world-leading competence in several research areas. We shape the future through innovative education and ground-breaking research results, and based on the Arctic region, we create global social benefit. Our scientific and artistic research and education are conducted in close collaboration with international, national and regional companies, public actors and leading universities. Luleå University of Technology has a total turnover of SEK 1.9 billion per year. We currently have 1,840 employees and 17,670 students.

In the coming years, multi-billion investments will be made in large projects in Northern Sweden to create a fossil-free society both nationally and globally. Luleå University of Technology is involved in several of these cutting-edge research projects and in the societal transformation that they entail. We offer a broad range of courses and study programmes to match the skills in demand. We hope that you will help us to build the sustainable companies and societies of the future.

Luleå University of Technology is experiencing rapid growth producing world- leading expertise within several research domains. We shape the future through innovative education and groundbreaking research results. From our location in the Arctic region, we aim to create global societal benefits. The Machine Learning Research subject at LTU has an open position available in the area of Sustainable Machine Learning. We offer state-of-the-art resources for performing research and a good academic network in Sweden and abroad. Our machine learning group is part of the Wallenberg AI, Autonomous Systems and Software Program (WASP). 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.

Subject description Machine learning focuses on computational methods by which computer systems uses data to improve their own performance, understanding, and to make accurate predictions and has a close connection to applications.

Project description The research projects in sustainable machine learning are focused on developing Edge AI solutions that harmonize environmental responsibility with technological progress. Central to these initiatives is the transformative potential of Tiny Machine Learning (TinyML) (https: // youtu.be/MgqcLCqqjuQ), which allows AI deployment on resource- constrained devices such as microcontrollers, enabling energy-efficient systems across the cloud-to-edge continuum. By leveraging heterogeneous hardware and enhancing software engineering processes and interoperability, these projects aim to create AI systems that are robust, explainable, and widely accessible. Additionally, they prioritize social and environmental responsibility, ensuring that sustainable AI development not only advances technological capabilities but also aligns with long-term societal and environmental welfare.

Duties The position involves conducting collaborative research with both academic institutions and industry partners across Europe, emphasizing strong industry engagement. Responsibilities include supervising doctoral students and contributing to research projects, from planning to execution and analysis. In addition to research, the role includes teaching and the development of educational programs, with a focus on enhancing pedagogical methods. Close collaboration with fellow researchers and interdisciplinary teamwork is also a key component of the role.

Qualifications To qualify for a position as a postdoctoral research, you must have a doctoral degree or an equivalent foreign degree in computer science, computer engineering, electrical engineering, artificial intelligence, embedded systems, mathematics, robotics, or a closely relevant field equivalent. A doctoral degree awarded no more than three years before the application deadline provides a useful qualification. Candidates who have been awarded a doctoral degree at an earlier date may also be considered if there are special grounds, for example, different types of statutory leave of absence. Research expertise in the Internet of Things, Embedded machine learning, Edge AI, or Tiny Machine Learning is beneficial. We are seeking enthusiastic candidates who can demonstrate expertise in conducting cutting-edge research, as evidenced by a record of peer-reviewed publications. Experience in international research project collaboration is highly desirable. Candidates should possess strong proficiency in English, both spoken and written, and have the ability to work independently as well as collaboratively in teams. The Machine Learning group is highly diverse, and we warmly welcome applicants from all backgrounds.

Further information The postdoc position is for two years with placement in Luleå, starting upon negotiation.

For further information about the position, please contact Senior Lecturer Dr. Hui Han, [email protected]

Union representatives: SACO-S Joanna Hübinette, (+46)920-49 3432 [email protected] OFR-S Lars Frisk, (+46)920-49 1792 [email protected]

In case of different interpretations of the English and Swedish versions of this announcement, the Swedish version takes precedence.

Application We prefer that you apply for this position by clicking on the apply button below. The application should include a CV, personal letter and copies of verified diplomas from high school and universities. Mark your application with the reference number below. Your application, including diplomas, must be written in English or Swedish.

Closing date for applications : October 31, 2024 Reference number: 3442-2024

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