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PhD Position in Generalized Driven Distributed Continuous Machine Learning
Project Description
Electrification and digitization is one of the biggest areas of the future shift towards a sustainable society. The Department of Electrical Engineering has been successful in the fields of renewable energy, electric vehicles, industrial IoT, artificial intelligence, 6G communications and wireless sensor networks, as well as in research and education in life sciences, smart electronic sensors and medical systems. The Department of Electrical Engineering is an international workplace with approximately 160 employees, all of whom contribute to important technological challenges in energy and health at the Ångström Laboratory.
The position will be working in the Signals and Systems department of the Faculty of Electrical Engineering. There is a friendly working environment and a strong research program. The Signals and Systems Department cooperates with public and private companies in Sweden and with stakeholders in different research areas. We look forward to receiving your application. Join us and build the future with us!
About the project Machine learning methods can usually only solve specially trained tasks. They first adapt (train) a mathematical model on a large number of examples and then apply the trained model. However, when the trained model encounters a new situation, its performance drops dramatically. In other words, these systems may not generalize well: they perform poorly in scenarios that are relevant but different from the trained ones. This poses a major obstacle to the effective and reliable use of machine learning in real-world applications.
We need to find training methods and model structures that can learn to master new situations without unduly forgetting previously learned knowledge. This project will investigate and develop such models that allow for continuous learning. We will focus on continuous learning in situations where multiple devices cooperate and learn together, i.e. distributed learning. Such situations are of great practical interest, but can make generalization more difficult to achieve. We will use structural insights from mathematical analyses of these problems to develop and evaluate continuous learning methods that enable generalization.
Responsibilities
The PhD student will conduct research in the area of distributed machine learning
PhD students will actively contribute to defining research questions for their PhD projects
They will be actively involved in the planning, implementation and necessary modifications of the research project. PhD students will gain advanced and up-to-date expertise in the field
They will develop new theories and methods and analyze the generality of these methods.
The PhD students’ work will also include writing scientific publications as well as oral presentations of research results in various venues such as project team meetings and international conferences.
The main task of a PhD student is to work on doctoral education, including participation in research projects and doctoral education programs. Duties also include participation in teaching and other institutional tasks up to a maximum of 20% of the working time.
RequirementsTo fulfill the requirements for admission to the Ph.D. program, you must
hold a master’s (second stage) degree in engineering physics, electrical engineering, machine learning, data science, computer science, applied mathematics or a similar field, or
have completed at least 240 credits of higher education in these fields, including at least 60 credits at the master’s level, including an independent project worth at least 15 credits, or
have otherwise acquired substantially equivalent knowledge.
Other Qualifications We are looking for candidates with the following qualifications:
Strong interest in developing new machine learning theories and methods
Strong math background
Proficiency in programming (preferably Python)
Good oral and written English skills
Organized, self-driven, independent approach to technical work and good collaboration skills
Emphasis on courses or other experience in the following disciplines: optimization, linear algebra, signal processing, probability, stochastic processes, statistical machine learning, and deep learning.
Regulations concerning PhD students are contained in Chapter 5, Sections 1-7 of the Higher Education Ordinance, as well as in the regulations and guidelines of Uppsala University.
With regard to employment the employment is a temporary position according to Chapter 5, Section 7 of the Higher Education Act. Start date: 2025-09-01 or as agreed. Place of work Uppsala
For more information about the position, please contact Ayca Ozcelikkale, [email protected]
Please submit your application by March 31, 2025 UFV-PA 2025/402
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offer requirements
To fulfill the requirements for admission to the PhD program you must
hold a master’s (second stage) degree in engineering physics, electrical engineering, machine learning, data science, computer science, applied mathematics or a similar field, or
have completed at least 240 credits of higher education in these fields, including at least 60 credits at the Master’s level, including an independent project worth at least 15 credits, or
have otherwise acquired substantially equivalent knowledge.
Other Qualifications We are looking for candidates with the following qualifications:
Strong interest in developing new machine learning theories and methods
Strong math background
Proficiency in programming (preferably Python)
Good oral and written English skills
Organized, self-driven, independent approach to technical work and good collaboration skills
Emphasis on courses or other experience in the following disciplines: optimization, linear algebra, signal processing, probability, stochastic processes, statistical machine learning, and deep learning.
offer benefits/salary
The PhD student will conduct research in the area of distributed machine learning
PhD students will actively contribute to defining research questions for their PhD projects
They will be actively involved in the planning, implementation and necessary modifications of the research project. PhD students will gain advanced and up-to-date expertise in the field
They will develop new theories and methods and analyze the generality of these methods.
The PhD students’ work will also include writing scientific publications as well as oral presentations of research results in various venues such as project team meetings and international conferences.
The main task of a PhD student is to work on doctoral education, including participation in research projects and doctoral education programs. Duties also include participation in teaching and other institutional assignments up to a maximum of 20% of working time.