Research topic: Privacy-aware transparent machine learning
Umeå University, the Department of Computing Science, is seeking candidates for four PhD student position in Computer Science with focus on data privacy. Deadline for application is April 15th, 2020.
The Department of Computing Science is a dynamic environment with around 120 employees from more than 20 countries worldwide. The Privacy-aware transparency decisions research group (led by Prof. Vicenç Torra) conducts research in data privacy for data to be used for machine and statistical learning. It is well known that data can be highly sensitive, and that naive anonymization is not sufficient to avoid disclosure. Models and aggregates can also lead to disclosure as they can contain traces of the data used in their computation. We want to understand the fundamental principles that permit us to build privacy-aware AI systems, and develop algorithms for this purpose. The group collaborates with several national and international research groups, edits one of the major journals on data privacy (Transactions on Data Privacy), and has active links with the private and public sectors. For more information see https://www.umu.se/en/research/groups/nausica-privacy-aware-transparent-decisions-group-/
The project is part of the Wallenberg AI, Autonomous Systems and Software Program (WASP), 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. Software is the main enabler in these systems, and is an integrated research theme of the program.
The vision of WASP is excellent research and competence in artificial intelligence, autonomous systems and software for the benefit of Swedish industry.”
Read more at: https://wasp-sweden.org/
The graduate school within WASP provides foundations, perspectives, and state-of-the-art knowledge in the different disciplines taught by leading researchers in the field. Through an ambitious program with research visits, partner universities, and visiting lecturers, the graduate school actively supports forming a strong multi-disciplinary and international professional network between PhD students, researchers and industry. It thus provides added value on top of the existing PhD programs at the partner universities, providing unique opportunities for students who are dedicated to achieving international research excellence with industrial relevance
We will develop machine learning algorithms that build data-driven models avoiding disclosure of private information and that are resistant to different types of attacks (t ex. transparency och membership attacks). The objective is to build statistical and machine learning models taking into account different types of privacy models (in particular differential and integral privacy), as well as different types of scenarios (centralized and decentralized data). Because of that, the project will consider centralized machine learning as well as federated learning approaches. Models are expected to follow trustworthy AI principles, and, in particular, take into account explainability. These models are attractive because they allow people to understand why decisions are made, but at the same time explainability implies additional privacy threats to be tackled.
The general admission requirements for doctoral studies are a second- cycle level degree, or completed course requirements of at least 240 ECTS credits, of which at least 60 ECTS credits are at second-cycle level, or have an equivalent education from abroad, or equivalent qualifications. To fulfil the specific entry requirements for doctoral studies in computing science, the applicant is required to have completed at least 90 ECTS credits in computing science, or in a subject considered to be directly relevant for the specialisation in question. Applicants who otherwise have acquired skills that are deemed equivalent are also eligible.
Candidates are expected to have very good knowledge of programming, especially for implementing efficient algorithms, and of mathematics and/or statistics. Demonstrable knowledge and experience on machine learning is an advantage.
Important personal qualities are, beside creativity and a curious mind, the ability to work both independently and collaborate in a group, and internationally. A very good command of the English language, both written and spoken, are key requirements.
Terms of employment
The position is aimed for PhD studies and research during four years, leading to a PhD exam. It is mainly devoted to postgraduate studies (at least 80% of the time), but may include up to 20% department service (usually teaching). If so, the total time for the position is extended accordingly (up to maximum five years). Expected starting date is 1st of August 2020 or as otherwise agreed.
A complete application should contain the following documents:
The application must be written in English or Swedish. Attached documents must be in Word or pdf format. Applications must be submitted electronically using the e-recruitment system of Umeå University, and be received no later than April 15th, 2020. Reference number: AN 2.2.1-270-20.
The procedure for recruitment for the position is in accordance with the Higher Education Ordinance (chapter 12, 2§) and the decision regarding the position cannot be appealed.
The Department of Computing Science values the qualities that an even gender distribution brings to the department, and therefore we particularly encourage women to apply for the position.
For additional information, please contact professor Vicenç Torra, firstname.lastname@example.org.
We look forward to receiving your application!
2020-08-01 or as otherwise agreed
Vicenç Torra, Professor email@example.com
Registration number AN 2-2-1-270-20
Salary Monthly salary
SACO 090-786 53 65
SEKO 090-786 52 96
ST 090-786 54 31