PhD student in Scientific Computing focusing on Secure Federated Machine Learning - Hiring in process/Finished, not possible to apply

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.

Project Description
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; And recently the focus has shifted towards security, privacy and trust based AI assisted solutions. Together with a number of other viable solutions, the federated machine learning (FedML) has proven to be a suitable approach for privacy-preserving machine learning. In this project, our focus will be on security and privacy-enhancing techniques for federated machine learning. The project addresses concerns related to security and privacy in federated machine learning against model poisoning and information leakage attacks. The approach is centered around developing new theories and methodologies to achieve two main aims, Secure aggregation of local models under poisoning attacks (Aim 1); Private distributed aggregation of local models (Aim 2).

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. 

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Duties
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.

Requirements
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 more of the following subjects: security and privacy, distributed systems, or machine learning. We expect successful candidates to have a high level of creativity, thoroughness and/or a structured approach.

Application
The application should include 1) a cover letter with a description of yourself, your research interests, reasons for applying for this particular PhD position and past experience (max. 3 pages), 2) a CV, 3) copies of relevant university degrees and transcripts of records, 4) relevant publications (if any), 5) a copy of your MSc thesis (or a draft in case the thesis is ongoing, and/or some other self-produced technical text) and 6) other relevant documents and certificates (if any, for example, participated in national or international level competitions). Candidates are encouraged to provide up to 3 letters of recommendation and contact information to reference persons. We would also like to know your earliest possible date for starting.

Additional qualifications
Extra merits with equal weights include familiarity with Federated Machine Learning, Multi-party Computations (MPC) and experience in numerical optimization, and best practices in software engineering.

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 2022-09-01 or as agreed. Placement: Uppsala.

For further information about the position, please contact: Associate Professor Salman Toor, Salman.Toor@it.uu.se, Associate Professor André Teixeira, andre.teixeira@it.uu.se.

Please submit your application by 27 May 2022, UFV-PA 2022/1729.

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