International PhD Course

Emerging Digital Technologies


2 MARIE CURIE FELLOWSHIP FOR EARLY-STAGE RESEARCHERS (ESRs) in the framework of the European MENTOR project.

Deadline for application submission: June 18th 2021, 12.00 p.m.  (ITALIAN TIME)

Title evaluation: June 28th, 2021

Interviews for MENTOR ESRs positions:  July 12th from 10.30 CET TIME


Optical networks underwent considerable changes over the past decade, as consequence of a continuous growth (exceeding 20% per year) of bandwidth demand. Multi band is a favorable solution to operators for network evolution. However, wide-band optical system presents new major challenges.

The European Industrial Doctorate MENTOR offers a timely proposal to train ESRs in the interdisciplinary field of high industrial importance: Machine Learning (ML) and Artificial Intelligence (AI) applications to optimize multi-band optical networks. MENTOR consortium offers the strong industrial commitment of four large companies (Infinera Germany and Portugal, ORANGE Labs and TELECOM ITALIA MOBILE) that significantly contributed in defining the research and training topics to be studied together with the world-leading academic partners in MENTOR.

MENTOR is funded by the Horizon 2020 Marie Skłodowska-Curie Action of the EU within the Innovative Training Networks (ITN) framework under Grant Agreement 956713.

The positions open at Scuola Superiore Sant’Anna refer to the following topics:

ESR3 – Management of optical network degradation via ML&AI

Supervisor: Dr. Nicola Sambo


In an ideal world, enough data and different type of dataset should be always available to guarantee the fully optimized planning and functioning of an ML&AI-based optical network management. Unfortunately, in a real-world, this is not necessarily the case. Data may be sometimes available, but often either there is only a sub-set or no data at all addressing a particular scenario available. Therefore, the training of ML&AI-algorithms will not be fully optimized and, consequently, it will not be able to respond adequately to some scenarios. We propose to investigate, via data augmentation, the possibility to extend our dataset consequently minimizing the energy and time needed to obtain real data. Additionally, we will evaluate the use of AI on the (re-) placement of network functions to satisfy specific KPIs (e.g. of performance, energy saving) considering various aspects including the hardware requirements. We will consider different use cases and verify the effectiveness of synthetic data (also generated via data augmentation) so that fundamental operations of optical networks such as provisioning, restoring, etc, are guaranteed. In this case, ML&AI techniques will be used in routing and traffic engineering. We will consider the proposed approach also in the contexts of failure detection, by implementing a predictive maintenance, with the minimization of the number of sensors / elements (we plan to address their replacement via data-augmentation) and latency reduction.

ESR4 – Automation in MB optical networks assisted by AI

Supervisor: Dr. Nicola Sambo


This project aims at designing RSA strategies during provisioning and recovery to be implemented in an SDN controller for wide band optical networks accounting for proper physical layer impairments (e.g., Raman scattering). The SDN controller will also decide – assisted by AI – for power levels considering the power transfer among the bands. The NETCONF configuration protocol based on YANG data modelling will be implemented for such networks and supporting the configurations decided by the proposed solutions into the SDN controller. The proposed control plane will be demonstrated in a control plane testbed and in an integrated data and control plan testbed.

Among the requirements, each applicant:

  • Must not have obtained a PhD degree yet

  • Did not reside in Italy for more than 12 months in the previous 3 years

  • Is in the first 4 years of his/her research career.

The research project must be developed around the 2 ESR research titles and objectives provided above.

For information on the scientific content of the research, please contact:

Dr. Nicola Sambo,


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Schedule of interviews
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