27/10/2020

One postdoctoral fellowship (2 years) in privacy-aware machine learning

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  • ORGANISATION NAME
    Umea University
  • ORGANISATION COUNTRY
    Sweden
  • FUNDING TYPE
    Funding
  • DEADLINE DATE
    25/11/2020
  • RESEARCH FIELD
    Formal sciences
    Humanities
    Natural sciences
    Professions and applied sciences
    Social sciences
  • CAREER STAGE
    Recognised Researcher (R2) (PhD holders or equivalent who are not yet fully independent)

Description

Umeå University announces one postdoctoral felloship for postdoctoral qualification on privacy-aware machine learning. Deadline for application is November 25, 2020.

The Department of Computing Science (http://www.cs.umu.se) is a dynamic department with about 130 employees from over twenty countries. We are providing research and education within a broad spectrum of areas, and offer education on basic, advanced, and PhD levels. The research is internationally well recognized and includes basic research, methods development, and software development, but also research and development within various application domains.

Project description

The currently announced stipend is connected to the Privacy-aware transparent decisions group. This 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 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-/

In the project 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 (e.g., transparency and membership attacks). We will develop solutions for centralized and decentralized machine learning (i.e., federated learning). 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 successful candidate are expected to actively collaborate with and support PhD students in the area of privacy-preserving machine learning.

The stipend project is financed by the Kempe foundations and associated with the Wallenberg AI, Autonomous Systems and Software Program (WASP). 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. 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 stipend is for two years with a starting date to be negotiated. The stipend amounts to 330 000 SEK per year (around 31500 euro). The stipend is not subject to tax.

Qualifications

A qualified applicant is required to have a PhD degree or a foreign degree that is deemed equivalent in Computer Science, Mathematics, Statistics or another subject of relevance for the project. The PhD degree shall not be more than three years old by the application deadline unless there are special reasons. The applicant should be strongly motivated and interested to develop new competences, as well as to act in an international environment.

Documented knowledge and proven research experiences in at least one of the areas federated learning, adversarial learning, large Markov chains, interpretability, provenance for machine learning, machine and statistical learning, data privacy is required.

A successful candidate should be capable of performing practical implementations of theoretical models as well as producing scientific publications in English. A very good command of the English language, both written and spoken, are key requirements.

Good research merits and scientific publications in the area of the position are strongly meriting. International research experience is also a merit.

Application

 

The application should include:

  • An introductory letter summarizing your qualifications, research interests, and motivation for applying (max 2 pages)
  • CV with list of publications
  • Copies of PhD thesis and relevant publications
  • Copies of doctoral degree certificate and other relevant degree certificates
  • Name and contact information for two or three reference persons
  • Any other relevant information, e.g. about competencies and / or experiences

 

Submit your application as a PDF marked with the reference number FS 2.1.6-2095-20, both in the file name and in the subject field of the email, to medel@diarie.umu.se. The application can be written in English (preferably) or Swedish. Application deadline is 25 November 2020.

Send also you application to vtorra@cs.umu.se.

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 stipend.

Further information

Further information is provided by Prof. Vicenç Torra, vtorra@cs.umu.se.
https://www.umu.se/en/work-with-us/fellowships-and-grants/6-2095-20/ 

 

We look forward to your application!

 

Disclaimer:

The responsibility for the funding offers published on this website, including the funding description, lies entirely with the publishing institutions. The application is handled uniquely by the employer, who is also fully responsible for the recruitment and selection processes.