1 dag geleden - Universiteit Utrecht (UU) - Utrecht
PhD position in the TUDelft-KPN collaboration NExTWORKx
Delft University of Technology and KPN, the leading fixed and mobile telecom operator in The Netherlands, have started a collaboration, called NExTWORKx. …
- Mekelweg, Delft, Zuid-Holland
- Tijdelijk contract / Tijdelijke opdracht
- Uren per week:
- 38 - 40 uur
- € 2222 - € 2840 per maand
Delft University of Technology and KPN, the leading fixed and mobile telecom operator in The Netherlands, have started a collaboration, called NExTWORKx. Goals of this collaboration include excellent academic research into both fundamental properties and implementation of the next generation telecommunication networks. In the first phase of the collaboration, 6 PhD students, daily supervised by experts in both TUDelft and KPN, will focus on themes that are relevant for KPN in order to design and manage the network of the future using promising technologies as Artificial Intelligence (AI), 5G and Blockchain.
The current PhD openings are defined on the following themes:
(a) 3 PhD positions on AI-networking,
(b) 2 PhD positions on 5G networking and
(c) 1 PhD position on Blockchain-inspired technology.
For more details please check the "Additional Information" at the bottom of this page
We are looking for brilliant PhD candidates with a strong interest to the join a university-company collaboration on future Networking. The specific desired background per theme is listed below.
Because of project definitions, we are looking for EU citizens.
TU Delft offers a customisable compensation package, a discount for health insurance and sport memberships, and a monthly work costs contribution. Flexible work schedules can be arranged. An International Children’s Centre offers childcare and an international primary school. Dual Career Services offers support to accompanying partners. Salary and benefits are in accordance with the Collective Labour Agreement for Dutch Universities.
As a PhD candidate you will be enrolled in the TU Delft Graduate School. TU Delft Graduate School provides an inspiring research environment; an excellent team of supervisors, academic staff and a mentor; and a Doctoral Education Programme aimed at developing your transferable, discipline-related and research skills. Please visit www.tudelft.nl/phd for more information.
To apply, please e-mail a detailed CV along with a letter of application by June 1, 2018 to Lotte Ophey at HR-EEMCS@tudelft.nl, with a clear choice for only 1 of the six themes listed below. When applying for this position, please refer to vacancy number EWI2018-22.
1. AI-networking: decisions and clustering
The ability to accurately discover all hidden relations between items that share similarities is paramount to solving large optimization problems that pertain to artificial intelligence and networking. By embedding our recently developed clustering techniques into reinforcement learning problems, we will optimize several processes within KPN that relate to learning, decision taking, recommendation giving, and prediction making.
Background: probability theory, stochastic processes, network science, decision theory
Supervisors: Jaron Sanders and Piet Van Mieghem; Network Architectures and Services (NAS)
More information: nas.ewi.tudelft.nl, jaronsanders.nl
2. AI-networking: control and network science
Based on network state information (e.g. in routers), the network’s dynamic process is identified using system’s theory and new learning methods in order to control and manage the telecom network.
Background: network science, systems theory, telecommunications, stochastic processes
Supervisors: Bart De Schutter (3ME, DCSC) and Piet Van Mieghem (NAS)
More information: nas.ewi.tudelft.nl, www.dcsc.tudelft.nl
3. AI-networking: data and network science
Automatic recommendation of operation choices to network/system components (e.g. which content, service, or resource to allocate) when the system is subject to heterogeneous and dynamic user demand.
Background: network science, data science (recommender systems, machine learning, time series analysis), complex systems
Supervisors: Huijuan Wang and Alan Hanjalic; Multimedia Computing Group
More information: www.mmc.tudelft.nl
4. 5G for Ultra Reliable Low Latency Communications (URLLC) for Smart Industry Applications
Mission-critical applications (e.g. autonomous and remotely controlled robots) require ultra-reliable and low-latency communications in often extremely challenging environments. Technological and network management solutions (access control, scheduling, device-to-device communications, link adaptation and edge computing) need to be designed.
Background: telecommunications, electrical / computer engineering, queueing theory / stochastic processes
Supervisors: Remco Litjens and Piet Van Mieghem (NAS)
5. 5G resilience through network programmability
To enable autonomous, self-healing 5G networks by leveraging current advances in network programmability and artificial intelligence.
Background: network programmability (incl. software-defined networking, programmable data-planes, and network functions virtualization) and algorithmics (incl. AI)
Supervisors: Fernando Kuipers and Koen Langendoen; Embedded Software group
6. Blockchain technology to provide trust in federated telecom and ICT-infrastructures
Adapting and applying blockchain technology to provide trust in the operation and management of telecommunication networks (e.g., 5G networks) and the users services (e.g., payment services) running on those networks.
Background: distributed algorithms, (programming) distributed systems, experimental evaluation of computer systems
Supervisors: Dick Epema and Johan Pouwelse; Distributed Systems Group
Link to press release on NExTWORKx [Dutch]