Job Details

Job Title

Developer or Researcher – Distributed Data-Driven Network Slice Management

Job Duration

Initial contract is for 12 months, extensible based on performance.

Job Starting date

As early as possible.

Job Background

5G networks will heavily rely on network slicing concept to enable better flexibility and service quality. Network slicing allows Mobile Network Operators (MNOs) to create, over the same physical network, multiple virtual networks, customized for different users, devices and applications having diverse quality of service requirements. In fact, network slicing empowers coexistence of highly diverse tenants and services in perceived isolation and allows efficient management of resource shared among them. Nevertheless, network management under network slicing gives rise to different challenges related to scalability, security, as well as automated management of heterogenous resources (e.g., communication, computational and storage). To cope with the massive deployment of network slices, it is required to build a highly-distributed, data-driven and zero-touch network management system, where advanced self-learning algorithms will enable a scalable, proactive and secure slice lifecycle management. Artificial Intelligence (AI) and Machine Learning (ML) mechanisms will play an important role to achieve this goal. Nevertheless, AI/ML models can be biased to make erroneous prediction and decision-making, potentially causing performance degradation and financial harm, as well as endangering SLA fulfilment and security guarantees. Thus, mechanisms to make AI-driven mechanisms secure and robust are necessary for their adoption in slice-based network management.

Job Requirements

We are looking for highly-motivated developers, young researchers and experienced postdoc researchers who are willing to conduct high-quality research works, developing efficient approaches and methods that leverage zero-touch management concept, AI/ML techniques, SDN/NFV technologies, and Adversarial Machine Learning for trustable distributed data-driven network slice management. Depending on the job level, candidates should have either MSc or Ph.D. degree in a related field with a focus on artificial intelligence, networking and communications, and cloud computing. We are seeking candidates with good analytical skills and relevant experience in any of the following: artificial intelligence, machine learning, and network virtualization. Skills in network security are a plus. The potential candidates should have expertise in programming languages (Python, C/C++, Java), ML frameworks (Tensorflow, Keras, Torch), cloud environments (Kubernetes, OpenStack), and different SDN technologies (e.g., ONOS and OpenDayLight). For the candidates applying for postdoc positions, they should have a good publications record in very good journals and conferences.

Research Group

The MOSA!C Lab is led by Prof. Tarik Taleb. The lab belongs to the Communications and Networking Department, School of Electrical Engineering, Aalto University. It consists of a group of highly-enthusiastic researchers with strong hands-on programming skills and expertise in different areas relevant to mobile networking, cloud computing, Internet of Things, and UAV. The lab is involved in a number of research projects funded by different industries, Business Finland, The European Commission, and Academy of Finland. MOSA!C Lab conducts high-quality research with high industrial applicability and contributes to open source projects.

How to Apply

The evaluation of the applications will start immediately and will continue until the positions are filled. Interested applicants should fill-in the form below and attach the following documents (pdf files only). Please note that incomplete applications will not be regarded.

  • Motivation letter.
  • Research vision on the topic.
  • Detailed CV including the list of publications, developed tools and softwares.
  • At least 3 recommendation letters.

Application Form

Full Name


Email Address


Short Bio


Curriculum Vitae


Motivation letter


Research Vision on the Topic


Recommendation Letters (merge at least three recommendations in one pdf file)

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