PhD student in Scientific Computing focusing on Secure Federated Machine Learning
The deadline for applying to this position has passed.
Uppsala University is a comprehensive research-intensive university with a strong international standing. Our ultimate goal is to conduct education and research of the highest quality and relevance to make a long-term difference in society. Our most important assets are all the individuals whose curiosity and dedication make Uppsala University one of Sweden’s most exciting workplaces. Uppsala University has over 54,000 students, more than 7,500 employees and a turnover of around SEK 8 billion.
The Department of Information Technology has a leading position in research and all levels of higher education. Today the department has 300 employees, including 120 academic staff and 110 full-time PhD students. The Department comprises research and education in a spectrum of areas within Computer Science, Information Technology and Scientific Computing. More than 4000 students take one or several courses offered by the Department each year.
The position is hosted by the Division of Scientific Computing within the Department of Information Technology. As one of the world’s largest focused research environments in Scientific Computing the research and education has a unique breadth, with large activities in classical scientific computing areas such as mathematical modeling, development and analysis of algorithms, scientific software development and high-performance computing. The division is currently in an expansive phase in new emerging areas such as cloud and fog computing, data science, and artificial intelligence, where it plays key roles in several new strategic initiatives at the University. The division currently hosts 20 PhD students and have awarded more than 80 doctorates. Several PhD alumni from the division are successful practitioners in the field of scientific computing and related areas, in industry as well as in academia around the world.
Our research group specializes in developing theory, methods and software for distributed computing infrastructures and machine learning. We have a wide network of collaborators, and there will be opportunities to work together with excellent researchers within Sweden and abroad.
The successful candidates will be integrated in the newly established Graduate School in Cybersecurity at the Department of Information Technology. The Graduate School provides an environment where students and researchers in cybersecurity and related areas work together, through a core PhD-level curriculum of joint courses and research activities. Regular seminars are planned with presentations from the school’s participants and by invited speakers. Active participation in the Graduate School activities is expected.
Artificial intelligence (AI) is at the core of modern-day applications. With the advent of massive datasets, the last two decades were dedicated to improve the mathematical modeling and training processes. Recently, a significant extent of the research focus has shifted towards security, privacy and trust of AI-assisted solutions. Among the recently developed solutions, federated machine learning (FedML) has proven to be a suitable approach for privacy-preserving machine learning.
The main objective of federated machine learning is to train a global machine learning model based on data that is distributed across heterogeneous data providers, but without requiring the raw data to leave the local data provider. While the FedML paradigm has been shown to preserve confidentiality of the data between the local data providers and the global model aggregator, there are still open challenges in ensuring that the training process is secure and private against malicious data providers, as well as to monitor the integrity of the training process without having access to the local data.
In this project, our focus will be on security and privacy-enhancing techniques for federated machine learning. The approach is centered around developing new theories and methodologies to achieve secure aggregation of federated machine learning models.
The project will run in a close collaboration between Assoc. Prof. Salman Toor from the Division of Scientific Computing as main supervisor and Assoc. Prof. André Teixeira from the Division of Systems and Control as co-supervisor.
The duties of a PhD student are primarily directed at their own research education, which lasts four years. The work may also involve, to a limited extent (ca 20%) other departmental duties, such as teaching undergraduate courses and administrative tasks – in which case the position may be extended to a maximum of five years.
A PhD position at the Division requires a completed (or near to completing) Master of Science, or equivalent, in a field that is relevant to the topic of the project, good communication skills with sufficient proficiency in oral and written English, as well as excellent study results. Additional requirements for this position include proficiency in programming (preferably in Python), as well as knowledge of computer science, with a focus on one or both of the following subjects: distributed systems and machine learning. We expect successful candidates to have a high level of creativity, thoroughness and/or a structured approach.
Extra merits with equal weights include familiarity with one or more of the following topics: security and privacy, federated machine learning, multi-party computations (MPC), and numerical optimization. Familiarity with best practices in software engineering is also a merit.
Rules governing PhD students are set out in the Higher Education Ordinance chapter 5, §§ 1-7 and in Uppsala University's rules and guidelines.
About the employment
The employment is a temporary position according to the Higher Education Ordinance chapter 5 § 7. Scope of employment 100 %. Starting date 2023-01-01 or as agreed. Placement: Uppsala
Please submit your application by 27 September 2022, UFV-PA 2022/3004.
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Submit your application through Uppsala University's recruitment system.
Placement: Department of Information Technology
Type of employment: Full time , Temporary position longer than 6 months
Pay: Fixed salary
Number of positions: 1
Working hours: 100 %
County: Uppsala län
Seko Universitetsklubben email@example.com
Number of reference: UFV-PA 2022/3004
Last application date: 2022-09-27
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