Job description
Work environment
Eindhoven University of Technology (https://www.tue.nl/en/) is one of Europe’s top technological universities, situated at the heart of a most innovative high-tech region. Thanks to a wealth of collaborations with industry and academic institutes, our research has real-world impact. In 2015, TU/e was ranked 106th in the Times Higher Educational World University ranking and 49th in the Shanghai ARWU ranking (engineering). TU/e has around 3,000 employees and 2,300 PhD students (half of which international, representing about 70 nationalities).
The candidate will work at the Department of Electrical Engineering (https://www.tue.nl/en/university/departments/electrical-engineering/). Within this department, research and education is done in domains of Telecommunication, Care and Cure, and Smart energy systems. The interfaculty Institute for Photonic Integration performs research in the area of broadband telecommunication, by investigating the potential of optical technologies. As a key member of IPI, the Electro-Optical Communication Systems (ECO) group focuses its research on optical communication system techniques, ranging from systems for ultra-high capacity long reach transmission (encompassing single-mode, multi-mode and multi-core fiber systems), ultra-fast (all-) optical packet switching nodes, high-density intra-data center networks, to multi-service flexible access and in-building networks (including radio-over-fibre and optical wireless communication). ECO participates in several national and international projects.
Project description
The PhD project is in collaboration with KPN as part of the strategic co-operation between KPN and TU/e, TU/e-KPN flagship. The PhD project aims at investigating and demonstrating an AI-based automatic control of next generation photonics networks to support heterogeneous traffic growth from Mobile Access Network (5G and beyond) connected to Datacenters and edge computing nodes. The massive 5G cell deployments, datacenters, and edge computing nodes as well as novel applications with different requirements in terms of connectivity, latency and reliability is forcing operators such as KPN to deploy a large amount of networks elements and IT resources. Control and manage of such massive network elements and IT resources deployed in photonic networks becomes crucial to optimize and adapt those massive resources and match the heterogenous applications with different requirements as well as minimize their energy consumption. This will demand for novel low latency and scalable control systems. Autonomous operations and zero-touch networking are expected to enable a cost-effective and sustainable rapid photonic network growth. Zero-touch photonic networking is still in its early stages and requires the development of autonomous cognitive networking that entails closed-control-loop implementation collecting, analyzing, and making decisions on the photonic networks. AI-assisted decision making algorithms are growing importance for high-capacity photonic systems as the technique of choice to solve complex NP hard problems. The objective of this project is the investigation of novel network control systems exploiting AI-assisted network and IT resources optimization and allocation algorithms. The control plane will extensively rely on physical layer abstraction and modelling, and pervasive telemetry data collection to feed AI algorithms in order to implement and demonstrate the zero-touch networking paradigm.
Tasks
The aim of the project is to develop and build an AI-assisted autonomous control system to support next generation real-time zero-touch photonic networks. To achieve this aim, specific procedures have to be designed to enable self-management, self-diagnosis, and self-optimization through monitoring the status of the photonic switches and transmission system as well the network traffic. All these procedures will be supported by new AI-empowered algorithms performing resource allocation and AI and telemetry-based monitoring providing predictive capabilities for innovative autonomous zero touch networks. The PhD candidate will design, develop, and experimentally assess an extensive physical layer abstraction and modelling of the optical network, including transmission system, photonic cross-connect switch, and IT resources. This includes the design and implementation of physical opto-electronic interfaces between the control plane and data plane in order to realize a pervasive telemetry data traffic collection from the physical optical network to feed the AI algorithms. Then the PhD candidate will develop novel AI-based algorithms for intelligent decision making embedding the developed model of the optical network as well as novel AI-based traffic prediction models to implement a proactive resource allocation and enhance the performance and resource utilization of the network. Based on the developed AI-algorithms, the PhD candidate will design a framework for an AI-assisted autonomous and dynamic network supporting real-time operations and zero-touch network, including the development of autonomous cognitive networking that entails closed-control-loop implementation collecting, analysing, making decisions, and acting on the network devices. The PhD candidate will demonstrate and assess in the ECO laboratories the system operation and performance of the AI-assisted ultimate zero-touch networking control and telemetry systems for the resource optimization and allocation of the optical network interconnecting datacenters and multiple edge computing nodes to mobile access networks (5G and beyond). The PhD candidate will contribute to the TU/e efforts in establishing collaboration with the other researchers in the project and contribute to the related project reporting, scientific publication and dissemination activities.
Job requirements
Candidate profile (essential)
- MSc degree in electrical engineering, telecommunications, applied physics or computer science
- Solid knowledge of optical fiber communications and communication systems
- Familiar with optical and electronic laboratory measurement equipment, optical fiber systems and components, semiconductor devices, and electronic circuits
- Solid theoretical and applied knowledge of machine learning
- Solid skills in one or two programming languages e.g. OMNET, Python, and VHDL, Verilog HDL FPGA programming
- Proven team-working capabilities and communication skills
- English proficiency (both verbal and written)
- Willingness to start as soon as possible
Candidate profile (desirable)
- Familiar with software simulation tools for optical fiber communications
- Knowledge of SDN/VNF is an asset
- Knowledge of photonic devices and circuits / integration technologies is appreciated
Conditions of employment
- A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
- A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
- To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
- To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (PROOF program).
- A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
- Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
- Should you come from abroad and comply with certain conditions, you can make use of the so-called ‘30% facility’, which permits you not to pay tax on 30% of your salary.
- A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
- Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.
Information and application
More information
Do you recognize yourself in this profile and would you like to know more? Please contact
dr. Nicola Calabretta, n.calabretta[at]tue.nl
For information about terms of employment, click here or contact Mrs. Linda van den Boomen, HR advisor, l.j.c.v.d.boomen[at]tue.nl.
Please visit www.tue.nl/jobs to find out more about working at TU/e!
Application
We invite you to submit a complete application by using the ‘apply now’-button on this page.
The application should include a:
- Cover letter in which you describe your motivation and qualifications for the position.
- Curriculum vitae, including a list of your publications, list of courses taken and grades obtained, awards and the contact information of three references.
- Two reference letters
- Proof of English language skills (if you are not a native speaker)
- Brief description of your MSc thesis.
We look forward to your application and will screen it as soon as we have received it.
Screening will continue until the position has been filled.
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