With the emergence of Network Functions Virtualization (NFV) and Software-Defined Networking (SDN), networks are becoming more and more agile. Network softwarization, enabled by network programmability and virtualization of network functions (e.g., 5G network functions), allows communication service providers to launch network services rapidly while driving down both operational and capital cost. Leveraging aforementioned technologies, networks can be sliced logically into multiple dedicated, mutually isolated, end-to-end virtual networks composed by a number of customizable software-defined functions, tailored to a given application or service. Thus, network elements and functions can be easily configured and reused in each network slice to meet its respective performance requirements, enabling new business opportunities via multi-service and multi-tenancy.

Sliced multi-tenant networks are considered as an essential feature of 5G and beyond systems. The management of elastic network slices poses additional challenges in service orchestration, dynamic resource allocation and scaling. To ensure that slices support the explicit service requirements on user experience and system performance expressed by specific Service-Level-Agreements (SLAs), communication service providers need to employ service and network intelligence and slice context awareness.  Lately the research and industrial communities promote the use of AI/ML algorithms for the creation, re-configuration and management of end-to-end slices to fulfill the service requirements. Standardization groups, such as the ETSI Experiential Networked Intelligence (ENI), are also working along the same directions. The goal of this thesis is to propose novel machine learning-based solutions for efficient, real-time end-to-end network slice management in 5G and beyond systems.

You will be embedded in the MNS group. The group focuses its researches on the fundamental architectural problems that arise from the interconnection of systems and of data flows. We look at the emerging architectures that can support the operations of the future Internet.

What are you going to do?

• Study and apply network slicing, softwarization and virtualization technologies that enable advanced automation, orchestration and service creation capabilities;
• get comfortable and confident with optimization and ML algorithms;
• investigate and design machine learning approaches and algorithms for resource allocation and SLA decomposition in 5G and beyond architectures;
• design and perform controlled experiments in a simulation environment or develop appropriate proof of concept(s) to benchmark the performance or validate the applicability of proposed approaches;
• complete and defend your PhD thesis;
• become active in the research community and collaborate within and outside the Informatics Institute;
• publish and present work regularly at international conferences, workshops, and journals;
• assist in teaching activities (labs) and in supervising bachelor and master students.

What do we require?

• A Master's degree (completed or near completion) in a relevant discipline, such as computer science, electrical and computer engineering, telecommunication engineering, security and network engineering, etc.;
• knowledge in optimization and machine learning, communication and networking, programming skills (python);
• interest in pursuing applied research;
• fluency in English;
• commitment to maintaining an inclusive, collaborative, diverse, supportive work environment.

Our offer

A temporary contract for 38 hours per week for the duration of 4 years (the initial contract will be for a period of 18 months and after satisfactory evaluation it will be extended for a total duration of 4 years). This should lead to a dissertation (PhD thesis). We will draft an educational plan that includes attendance of courses and (international) meetings. We also expect you to assist in teaching undergraduates and master students.

The salary, depending on relevant experience before the beginning of the employment contract, will be €2,395 to €3,061 (scale P) gross per month, based on a full-time contract of 38 hours a week. This is exclusive 8% holiday allowance and 8,3% end-of-year bonus. A favourable tax agreement, the ‘30% ruling’, may apply to non-Dutch applicants. The Collective Labour Agreement of Dutch Universities is applicable.

Are you curious about our extensive package of secondary employment benefits like our excellent opportunities for study and development? Then find out more about working at the Faculty of Science.

University of Amsterdam

With over 5,000 employees, 30,000 students and a budget of more than 600 million euros, the University of Amsterdam (UvA) is an intellectual hub within the Netherlands. Teaching and research at the UvA are conducted within seven faculties: Humanities, Social and Behavioural Sciences, Economics and Business, Law, Science, Medicine and Dentistry. Housed on four city campuses in or near the heart of Amsterdam, where disciplines come together and interact, the faculties have close links with thousands of researchers and hundreds of institutions at home and abroad.  

The UvA’s students and employees are independent thinkers, competent rebels who dare to question dogmas and aren’t satisfied with easy answers and standard solutions. To work at the UvA is to work in an independent, creative, innovative and international climate characterised by an open atmosphere and a genuine engagement with the city of Amsterdam and society.

Faculty of Science - Informatics Institute

The Faculty of Science has a student body of around 7,000, as well as 1,600 members of staff working in education, research or support services. Researchers and students at the Faculty of Science are fascinated by every aspect of how the world works, be it elementary particles, the birth of the universe or the functioning of the brain.

The mission of the Informatics Institute is to perform curiosity-driven and use-inspired fundamental research in Computer Science. The main research themes are Artificial Intelligence, Computational Science and Systems and Network Engineering. Our research involves complex information systems at large, with a focus on collaborative, data driven, computational and intelligent systems, all with a strong interactive component.