Postdoctoral Fellow in Information Theory/Machine Learning

Do you want to dive into the exciting field of distributed machine learning with a special focus on privacy/security? This research topic is going to shake up how we understand and apply machine learning, with the aim of creating safer and more private solutions in an increasingly digitized world. Successful applicants will have the opportunity to explore and contribute to groundbreaking research questions. While specific research questions will be discussed with the successful applicant, they may include themes such as federated learning, fully decentralized learning, straggler mitigation, graph neural networks, and differential privacy. This is not just an opportunity to develop and shape your own research project, but also to help shape the future of machine learning and privacy. 

Simula UiB currently has 12 early career researchers working on a range of research problems in cryptography and information theory. We can offer a vibrant, stimulating, and inclusive working environment to successful candidates. The position is for two years, with a possible extension of one year. 

Simula UiB offers:

• Excellent opportunities for conducting high-quality research, as part of a highly competent and motivated team of researchers.
• An informal, inclusive and international working environment.
• Support for travel and opportunities to build international networks.
• Modern office facilities located at Marineholmen in the city centre of Bergen.
• A competitive salary; starting salary from NOK 575 400.
• Numerous employee benefits, including access to a company cabin, sponsored social events, an equipment budget, and a comprehensive travel/health insurance policy.
• Relocation assistance, including complimentary Norwegian language courses.
• Healthy wellness and work-life balance arrangements.

For full consideration, please submit applications no later than December 31, 2024, using the following site: https://www.simula.no/careers/job-openings/postdoctoral-fellow-in-information-theorymachine-learning