PhD Fellowships in Machine Learning: The Department of Computer Science at the University of Copenhagen is inviting applications for three PhD fellowships in the area of Machine Learning. This opportunity focuses on innovative research in Trustworthy Machine Learning, Robust Online Learning, and Sustainable Machine Learning. Successful candidates will contribute to advancing knowledge in these critical areas.
Designation
PhD Fellowships in Machine Learning
Research Areas
| Position Title | Description |
|---|---|
| 1. Trustworthy Machine Learning | Focus on robustness, privacy (specifically Differential Privacy), and unlearning in ML, exploring theoretical and practical aspects. |
| 2. Robust Online Learning | Investigate adversarial threats in online learning, especially in federated settings, to develop robust algorithms for real-time adaptations. |
| 3. Sustainable Machine Learning | Explore resource-efficient ML methods and their impact on sustainability, safety, fairness, and access linked to UN sustainable development goals. |
Location
Department of Computer Science, University of Copenhagen, Copenhagen, Denmark
Eligibility/Qualification
- General Eligibility:
- A degree equivalent to a relevant Danish master’s degree for regular PhD programme or a bachelor’s degree for the integrated MSc and PhD programme.
- Specific Requirements:
- Trustworthy ML: Strong mathematics background (probability, statistics, linear algebra), programming experience in ML.
- Robust Online Learning: Strong background in ML and optimization, knowledge of distributed algorithms and stochastic processes preferred.
- Sustainable ML: Strong skills in mathematics and ML with a commitment to sustainability.
Description
Candidates for these positions will engage in independent research under supervision, complete PhD courses, conduct teaching activities, and publish in high-impact journals. The department offers a vibrant international research environment and is well-ranked among European universities.
How to Apply
- Submit your application electronically through the “APPLY NOW” link on the department’s webpage.
- Include the following documents:
- Motivated letter of application (max. two pages) clearly stating the project you are applying for.
- Curriculum vitae with education, experience, language skills, and three referees.
- Original diplomas (Bachelor and Master) and transcripts (or certified recent transcripts).
- Publication list (if applicable).
Last Date to Apply
15th January 2025, 23:59 CET
For additional information, candidates are encouraged to contact the respective supervisors for each project.
