PhD Research Fellow in ICT - Signal Processing and Artificial Intelligence for Wireless Networks
University of Agder Faculty of Engineering and Science

Title PhD Research Fellow in ICT - Signal Processing and Artificial Intelligence for Wireless Networks
Employer University of Agder (UiA)
Job location Gimlemoen 25, 4630 Kristiansand S
Published August 23, 2019
Application deadline September 13, 2019
Job types PhD
Fields Algebra, Algorithms, Statistics, Communication Engineering, Industrial Engineering, Artificial Intelligence, Computer Communications (Networks), Programming Languages, Telecommunications Engineering


About the position
A 100 % position is avaiCenterle at the University of Agder, Faculty of Engineering and Science as a PhD Research Fellow in ICT, affiliated to the Department of Information and Communication Technology, for a period of three years. The position is located, at the present, at Campus Grimstad.

The starting date is as soon as possible or to be negotiated with the Faculty.


The herein announced position will be part of the recently established Center Intelligent Signal Processing & Wireless Networks (WISENET). The activities of the Center span across the Department of Information and Communication Technology and the Department of Engineering. The researchers in the WISENET Center have a strong expertise in a range of areas, among them, Data Science, Machine Learning, In-Network Signal Processing and Distributed Artificial Intelligence (AI) for Wireless Communications, Networked Cyber-Physical Systems and Embedded Systems. The Center has led a number of large research projects, funded by the Research Council of Norway, the EU research Programs FP7 and H2020, as well as national and international industries. The WISENET Center is in rapid expansion, having at present twelve PhD students and five postdoctoral researchers working on multiple cutting-edge research projects, such as FRIPRO TOPPFORSK, SFI, PETROMAKS, IKTPLUSS and INFRASTRUCTURE projects. WISENET Center is committed to achieving international research excellence; please see the notes about prospective PhD students at WISENET here before applying.

The open PhD position is offered in the area of AI-enabled self-organized wireless networks enabled by direct device-to-device (D2D) simultaneous links employing novel dynamic spectrum access (DSA) features, aiming at progressing substantially over the state-of-the-art both in terms of theoretical aspects (fundamental performance analysis) and algorithm designs. The activities are mainly driven by a multi-disciplinary advanced research project on next generation wireless communications enabled by self-organized spectrum cartography (WISECART). WISECART is funded by the Research Council of Norway (RCN) under the prestigious research funding framework "FRIPRO Toppforsk'', which is the most competitive and highly reputed funding program from the RCN for fundamental research across all disciplines.


Research topics for the position

The ever-increasing demand for ultra-high data rates and energy efficiency in radio access networks (RAN) together with the need for enabling different types of user-driven applications poses enormous challenges. In this context, D2D communication is being investigated in the wireless communication research community as a promising paradigm to boost both spectrum utilization and energy efficiency. To this end, D2D communication exploits the spatial proximity of devices, which enables direct exchanges of data and control information, i.e., bypassing the base station. As a consequence, higher spectrum reuse can be achieved by enabling multiple D2D communication links while meeting the communication needs for different types of services or applications involving nearby users. The design of the algorithms will involve also advanced machine learning techniques for predicting (or estimating) channel or network states, as well as for resource allocation, both in centralized and decentralized settings.

WISECART is a multidisciplinary project where the main goal is to scale up D2D communication to enable a self-organized networking among multiple and heterogeneous devices for purposes such as sharing contents of common interest, increasing connectivity, or intelligently performing certain cooperative tasks. These networks are expected to include not only human operated devices but also machine type communication devices and other objects, as motivated by the paradigms of Internet of Things (IoT) and Cyber-Physical Systems (CPS).

Your research may cover physical, MAC and network layers. In addition to theoretical advancements and algorithmic design, the PhD position will involve simulation and experimental validation.


The main topics for the PhD position will be:

High-resolution localization-based and localization-free methods to enable the construction of dynamic spectrum cartography maps (spectrum awareness), by solely relying on the information-bearing signals. This dramatically reduces the overhead of control traffic, which needs to be kept minimal so that the complexity well as a function of the number of nodes. The algorithms will be attuned to the specific user activity and traffic patterns and will involve data-driven statistical learning techniques and deep learning methods. 5G and next-generation waveforms are of special interest to this task.
Coordination protocols to build, maintain and adapt D2D-based topologies of sensors that are used to maintain the necessary spectrum awareness, including: (i) neighbor and topology discovery protocols; (ii) sampling protocols capable of dealing with irregular and timevarying sampling caused e.g. by loss of connectivity, node mobility, and synchronization issues; (iii) efficient distributed in-network computation, machine learning techniques and storage of interference maps, minimizing local storage requirements and maximizing the efficiency for accessing interference maps.
Adaptive user-driven network protocols: (i) Control-plane network protocols to support the generation and maintenance of mesh-type formations for local communications with low control overhead, by considering the trade-off between the level of automatization of the local topologies among the nodes that find themselves co-located and engaged in a given application, and the control that should be guaranteed to the users over the activities of their devices; (ii) Data-plane network protocols to provide specific MAC and routing algorithms for communication within mesh-type formations, considering application requirements and traffic patterns, such as distribution of multimedia content and distributed computation. The solution of the associated resource allocation problems will incorporate also machine learning algorithms. We will design protocols for scheduling and in-network caching in delay tolerant scenarios, making use of distributed dynamic storage at nodes and ensuring that transmission resources among the nodes are used according to the link qualities (e.g. channel gain maps), demand profiles, storage capacity and energy availability at nodes. We will also explore the use of mm-waves for communications to nearby nodes.
The PhD position will cover at least two of the topics described above. In addition, the work will involve also the development of some software architectures that will be used to implement the designed algorithms.


Required qualifications
To be regarded as an eligible applicant, the candidate must have:

A solid academic background with a MSc. in Electrical Engineering, Electronics Engineering, Communications Engineering, Industrial Engineering, ICT or equivalent, is required. It is also possible to apply if the applicant is in the last year of the Master studies and in this case, if the applicant is selected, she or he will start the PhD position once the Master degree is finished.
Substantial knowledge of all or most of the following:
optimization techniques
machine learning techniques
stochastic processes
statistical signal processing
wireless sensor networks
mathematical analysis and linear algebra
strong programming skills mainly in Matlab, Python, C/C++ and Java.
Strong academic credentials
Written and spoken English proficiency. Applicants from some countries must document their English proficiency through one of the following tests or certificates:
TOEFL - Test of English as a Foreign Language with a minimum score of 600 on the Paper-based Test (PBT), or a minimum of 92 on the Internet based Test (iBT)
IELTS - International English Language Testing System, with a result of at least 6.5, with no section lower than 5.5. (only Academic IELTS test accepted)
CEFR (Common European Framework of Reference for Languages) certificate of at least Level B2
Please check this website to see if an English test is required. Please note that the English test requirement applies to applicants from most countries according to the list mentioned above. No other English tests or certificates will be approved, and certifications/statements cannot replace an English test.

A prerequisite for employment is that the candidate is to be admitted to UiA's PhD programme at the Faculty of Engineering and Science, specialisation in ICT.

Further provisions relating to the position as PhD Research Fellow can be found in the Regulations Concerning Terms and Conditions of Employment for the post of Post-Doctoral Research Fellow, Research Fellow, Research Assistant and Resident.


Desired qualification
It would be considered an advantage to have additional knowledge and experience in:

D2D wireless communications
Radio resource management, interference management
Cognitive radios, DSA and spectrum sensing
Localization and navigation techniques
Distributed signal processing on graphs
Deep learning
RF measurements and implementation in Software-Defined Radio Test-beds
Previous participation in national and European projects related to the areas of this position, will be also considered as a plus, as well as the publication of scientific papers on international conferences related to these topics.
Personal qualities
Scientific ambition
Motivation and strong interest in cutting-edge research
Good analytical and problem solving skills
Capacity for goal-oriented work and ability to concentrate
Good communication and team-working skills, inventiveness and a proactive attitude
Personal qualities and suitability for the position will be emphasised.

Fakultet for teknologi og realfag


We offer
Professional development in a large, exciting and socially influential organisation
A positive, inclusive and diverse working environment
Modern facilities and a comprehensive set of welfare offers
Membership of the Norwegian Public Service Pension Fund
More about working at UiA.

The position is remunerated according to the State Salary Scale, salary plan 17.515, code 1017 PhD Research Fellow, NOK 479 600 gross salary per year. A compulsory pension contribution to the Norwegian Public Service Pension Fund is deducted from the pay according to current statutory provisions.

General information
A good working environment is characterised by its diversity. We therefore encourage all qualified candidates to apply for the position, irrespective of gender, age, disability or cultural background. The University of Agder is an IW (Inclusive Workplace).

Women are strongly encouraged to apply for the position.

The successful applicant will have rights and obligations in accordance with the current regulations for the position, and organisational changes and changes in the duties and responsibilities of the position must be expected. Appointment is made by the University of Agder's Appointments Committee for Teaching and Research Positions.


Read more about the employment process.

In accordance with the Freedom of Information Act § 25 (2), applicants may request that they are not identified in the open list of applicants. The University, however, reserves the right to publish the names of applicants. Applicants will be advised of the University's intention to exercise this right.

The application and any necessary information about education and experience (including diplomas and certificates) are to be sent electronically. Use the link "Apply for this job".

The following documentation must be uploaded electronically:

Justification (maximum 5 pages) of the background of the candidate for each of the requirements of the position (see description above about the knowledge areas that a candidate should have), especially for optimization techniques, stochastic processes and statistical signal processing, wireless networks and programming in MatCenter, CenterView and C/C++
Certificates and/or grades for all post-secondary education, up to and including the bachelor's level
Master's degree/higher degree certificate, with a summary of the courses/subjects included in the degree
Applicants with a foreign higher education must attach an official description of the grading system used at the issuing institution
Summary (approximately 1-2 pages) of the Master Thesis (if any)
Applicants who are required to document their English proficiency must submit their TOEFL or IELTS test results (these may be forwarded after the closing date) or their CEFR certificate
Publication list or links to applicant's scientific publications (if any)
A description of the candidate's research interests, motivation and background for the PhD position. Please, indicate clearly in your application your preferred topic/topics
A list with the names and contact information of reference persons who would be willing to be contacted by telephone
The applicant is fully responsible for submitting complete digital documentation before the closing date. All documentation must be avaiCenterle in a Scandinavian language or English.


Application deadline: 13.09.19

For questions about the position:

Professor Baltasar Beferull-Lozano, tel. +47 37 23 31 59, e-mail
Prof. Linga Reddy Cenkeramaddi, tel. +47 37 23 34 36 , e-mail:
Head of Department Professor Folke Haugland, tel. +47 37 23 31 12, e-mail
For questions about the application process:

HR Advisor Nina Rønningen, tel. +47 38 14 20 16, e-mail

If you apply for this position please say you saw it on Computeroxy


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