PhD student in Computing Science with focus on data privacy - Befattningen är under tillsättning eller redan tillsatt, och det är inte längre möjligt att lämna in en ansökan

To our department, characterized by world-leading research in several scientific fields and a multitude of educations ranked highly in international comparison, we now look for a doctoral student in Privacy-aware transparent machine learning.

The Department of Computing Science has been growing rapidly in recent years where inclusivity and a bottom-up driven environment are key elements in our sustainable growth. The 50 doctoral students within the department consists of a diverse group from different nationalities, background and fields. As a doctoral student with us you receive the benefits of support in career development, networking, administrative and technical support functions along with good employment conditions. See more information at:
https://www.umu.se/en/department-of-computing-science/

Is this interesting for you? Welcome with your application before 24 May 2022.

WASP
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

Project description

This is a project in collaboration with Chalmers and KTH. 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 and membership attacks). The objective is to build statistical and machine learning models taking into account different types of privacy models (differential and integral privacy, k-anonymity), as well as different types of scenarios (centralized and decentralized data). The project will consider 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. Data-driven models are to be applied to IoT (Internet of Things) scenarios.

Research group

The Privacy-aware transparency decisions research group (led by Professor 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-/

Admission requirements

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.  Applicants who otherwise have acquired skills that are deemed equivalent are also eligible.

Candidates are expected to have very good programming skills, especially for implementing efficient algorithms, and of mathematics and/or statistics. A very good command of the English language, both written and spoken, is a key requirement.

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 collaboratively in an international context.

About the position

The position provides you with the opportunity to pursue PhD studies in Computing Science for four years, with the goal of achieving the degree of Doctor in Computing Science. While the position is mainly devoted to PhD studies (at least 80% of the time), it may include up to 20% department service (usually teaching). If so, the total time for the position is extended accordingly, resulting in a maximum of five years.

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 expected starting date is August 1st or as otherwise agreed.

Application

Applications must be submitted electronically using the e-recruitment system of Umeå University.

A complete application should contain the following documents:

• A cover letter including a description of your research interests, your reasons to apply for the position, and your contact information
• A curriculum vitae
• Reprints / copies of completed BSc and/or MSc theses and other relevant publications, if any
• Verified copies of degree certificates, including documentation of completed academic courses and obtained grades
• Documentation and description of other relevant experiences or competences.

The application must be written in English or Swedish. Attached documents must be in pdf format. Applications must be submitted electronically using the e-recruitment system of Umeå University, and be received no later than 24 May 2022.

For additional information, please contact Professor Vicenc Torra (vtorra@cs.umu.se).

We look forward to receiving your application!