PhD Student in Machine Learning with a focus on Sustainable Machine Learning
PhD @Luleå University of Technology posted 20 hours agoJob 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 on sustainable machine learning focus on using Edge AI and Tiny Machine Learning (TinyML) (https://youtu.be/MgqcLCqqjuQ) to create efficient, low-power models that can operate on edge devices with limited computational resources. By leveraging Edge AI, these projects aim to process data locally, reducing the need for data transmission to centralized servers, which in turn lowers energy consumption and latency. TinyML further enhances sustainability by enabling the deployment of machine learning models on microcontrollers and other highly resource-constrained devices. This approach not only minimizes the environmental impact of AI systems but also democratizes access to AI technologies, allowing for widespread implementation in various applications, from smart cities to remote sensing, all while maintaining a focus on reducing the overall carbon footprint and promoting ecological responsibility. As a PhD student, you will join our Machine Learning group in Sustainable Machine Learning. As part of our dynamic research group, you will spearhead innovative initiatives at the forefront of sustainability and artificial intelligence, driving forward ground-breaking advancements with real-world significance.
Duties
As a PhD student you are expected to perform both experimental and theoretical work within your research studies as well as communicate your results at national and international conferences and in scientific journals. Most of your working time will be devoted to your own research studies. In addition, you can have the opportunity to try the teacher role. As a researcher, you work as a neutral party in many contexts, which provides a great opportunity to be involved in challenging development projects.
This PH.D. student position is associated with the Department of Computer Science and Electrical and Space Engineering at the Luleå University of Technology under the Wallenberg AI, Autonomous Systems and Software Program (WASP) funding.
Qualifications
We seek a highly motivated doctoral student eager to make a difference in sustainable machine learning. Candidates should have a master’s degree in computer science, engineering physics, electrical engineering, data science, mathematics, or similar. We expect candidates to have previous experience in areas such as artificial intelligence, embedded systems, robotics, or a closely relevant field equivalent. Furthermore, candidates should have very good programming skills and high proficiency in oral and written communication in English. Experience in IoT and real word experimentation will be highly considered as a merit.
For further information about a specific subject see
General syllabus for the Board of the faculty of science and technology
Information
Employment as a PhD student is limited to 4 years, teaching and other department duties may be added with max 20%.
Entrance to the position: enrolment starting as soon as possible or by agreement. Location: Luleå.
For further information, please contact Senior Lecturer Dr. Hui Han, (+46)920-49 1059 hui.han@ltu.se
Union representatives:
SACO-S Diana Chroneer, (+46)920-49 2037 diana.chroneer@ltu.se
OFR-S Marika Vesterberg, (+46)920-49 1721 marika.versterberg@ltu.se
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, a copy of the master’s thesis (if the thesis is not written in English or Swedish, you are asked to additionally submit an English summary of the thesis, 1-2 pages) and copies of verified diplomas from high school and universities. Your application, including diplomas, must be written in English or Swedish. Mark your application with the reference number below.
