Greenedge: 15 fully funded PhD positions (Marie Skłodowska Curie) 2016 views

Greenedge is a Marie Skłodowska Curie Innovative Training Network (ITN) funded by the European Commission. Its chief goal is to promote, design and implement machine learning based computing systems for the mobile edge that are highly energy efficient. The duration of the contract is 36 months. The ESR fellows are expected to join their host organizations starting from September 2021 (estimated). The ESR is covered under the social security scheme that applies to employed workers within the country of the host organization, or under a social security scheme providing at least sickness and maternity benefits in kind, invalidity and accidents at work and occupational diseases, and covering the researcher in every place of implementation of the project activities.

Requirements:

• Applicants should be, at the time of recruitment by the host institution, in the first four years (full-time equivalent) of their research careers and have not yet been awarded a doctoral degree. This is measured from the date when they obtained the degree, which would formally entitle them to embark on a doctorate.
• At the time of recruitment, the applicant must not have lived in the country where the position is offered for more than 12 months in the previous 36 months.

How to apply:

Apply online and the applicants must fill the online application form, uploading the following documentation:

• ID: Xeroxed copy of a valid ID (passport);
• CV – Europass format
• Academic record: BS and MS exams with scores (list)
• Personal statement (e.g., one page with background & motivation)
• Reference letters from two academic/research supervisors (this is the preferred option. If not possible, please specify their emails in your CV and we will contact them ourselves).
• ESR projects: selection of up to 3 projects, in order of preference
• Proof of eligibility
• Recommended: Master’s degree certificate

ESR projects:

ESR1: Elastic allocation of edge computing resources in EH-MEC networks (host: UNIPD)

ESR2: Hierarchical learning for the online control of edge network protocols (host: UNIPD)

ESR3: Edge network function activation and placement via neuro-inspired algorithms (host: UNIPD)

ESR4: Orchestration of deep learning models for communication and computing across EH edge nodes (host: CTTC)

ESR5: Energy-efficient edge computing for sustainable metropolitan areas (host: CTTC)

ESR6: Energy-efficient federated learning at the wireless network edge (host: Imperial College London)

ESR7: Energy-efficient coding and modulation for wireless edge learning (host: Imperial College London)

ESR8: Semi-supervised edge energy consumption anomaly detection and classification (host: KUL)

ESR9: Intermittent computing for the extreme edge in beyond 5G networks (host: KUL)

ESR10: Edge computing and communications for Light-based IoT (LIoT) (host: OULU)

ESR11: Distributed intrusion detection system in a constrained edge context (host: CEA-List)

ESR12: Mobility aware communication, computation and caching-based resource allocation for MEC (host: CEA-Leti)

ESR13: Edge operational intelligence for critical infrastructure management (host: Worldsensing)

ESR14: End-to-end communication/computing resource management for low-latency beyond 5G MEC (host: CTTC)

ESR15: Distributed ML for radar localization with multiple nodes (host: TOSHIBA)