Postdoc in Privacy and Robustness of Machine Learning Algorithms

Department of Computer Science
Faculty of Science
University of Copenhagen
Machine learning algorithms are increasingly being deployed in real-world systems that utilize user data for training or interact directly with individuals—often making decisions that have significant impacts on people's lives. In this context, ensuring the trustworthiness of these systems, particularly concerning privacy, unlearning, and/or robustness, is important. We are seeking a highly motivated postdoctoral researcher to conduct theoretical and/or empirical research on privacy and/or robustness in machine learning.

Potential research topics include, but are not limited to:

- Learning-theoretic implications of differential privacy, machine unlearning, or robustness and the relationship between these notions

- Developing statistically and computationally efficient algorithms that incorporate these properties

- Exploring privacy, unlearning, and/or robustness in alternative learning settings such as online, active, semi-supervised, or noisy environments

We highly encourage applicants with fresh ideas and interests beyond these specific topics, but related to the broader description of privacy and robustness, to apply.

The successful candidate will be based at the Department of Computer Science at the University of Copenhagen and will work primarily with Amartya Sanyal. Their main duty will be to conduct novel research in the aforementioned areas, collaborate with other members in the research group, and publish findings in leading venues such as NeurIPS, COLT, ALT, ICML, SODA, FOCS, AISTATS, UAI, SaTML, and ICLR. Additional responsibilities may include collaborating with other research groups internationally and domestically as well as participating in other related duties as needed. Inquiries about the position can be made to Amartya Sanyal ([email protected]).

The position is open from June, 2025 or as soon as possible thereafter. Earlier start date can be discussed. The length of the employment is 2 years.

Further information on the Department can be found at https://di.ku.dk/english/.

The University wishes our staff to reflect the diversity of society and thus welcomes applications from all qualified candidates regardless of personal background.

Position requirements:

• (Required) PhD degree in computer science, statistics, mathematics, or similar
• (Required) Experience with some aspects of learning theory, differential privacy, or robustness.
• (Required) Basic Programming skills in Python.
• (Required) High level of motivation with the ability to conduct independent research by identifying key problems and driving projects to completion.
• (Required) Proven skills in writing high-quality research papers and delivering effective presentations.
• (Optional) Experience in empirical privacy and robustness, training of machine learning models, or the desire to get involved in it.
• (Optional) Familiarity with some aspects of machine unlearning.

Terms of employment
The position is covered by the Memorandum on Job Structure for Academic Staff.

Terms of appointment and payment accord to the agreement between the Ministry of Finance and The Danish Confederation of Professional Associations on Academics in the State.

Negotiation for salary supplement is possible.

The application, in English, must be submitted electronically by clicking APPLY NOW below.

Please include:

• Curriculum vitae
• Diplomas (Master and PhD degree or equivalent)
• Research plan – description of current and future research plans.
• Complete publication list
• Separate reprints of 3 particularly relevant papers

The deadline for applications is 15 December 2024, 23:59 GMT +1.
After the expiry of the deadline for applications, the authorized recruitment manager selects applicants for assessment on the advice of the Interview Committee.

You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/.
Interviews will be held in January 2025.

Københavns Universitet giver sine knap 10.000 medarbejdere muligheder for at udnytte deres talent fuldt ud i et ambitiøst, uformelt miljø. Vi sikrer traditionsrige og moderne rammer om uddannelser og fri forskning på højt internationalt niveau. Vi søger svar og løsninger på fælles problemer og gør ny viden tilgængelig og nyttig for andre.

Info
Ansøgningsfrist: 15-12-2024

Ansættelsesdato: 01-06-2025

Arbejdstid: Fuldtid

Afdeling/Sted: Universitetsparken 5, KBH Ø