WindMill: 15 PhD (ESR) positions; Wireless Communications and Machine Learning 2077 views

WindMill is a European Training Network (ETN) project, within the framework of the H2020 Marie Skłodowska-Curie Innovative Training Networks (ITNs). The research in WindMill is about the integration of two research fields: wireless communications and machine learning. The overall research objective is the development of new methodologies based on machine learning in the design of wireless systems, while also contributing to the advancement of applied ML science.

Duration: Full-time 36 months.

Research areas:

• Machine learning
• Wireless communication networks, with focus on 5G and post 5G.
• Application of machine learning to wireless network design.

Eligibility Requirements

• Applicant must, at the time of recruitment by the host organisation be in the first four years (full-time equivalent research experience) of their research careers and not yet have been awarded a doctoral degree.
• Applicant must not have resided or carried out their main activity (work, studies, etc.) in the country of their host organisation for more than 12 months in the three years immediately prior to the recruitment date.
• There is no eligibility condition on the nationality of the candidate.
• Non EU citizens are eligible to apply.

Available projects:

1. Adversarial Variational Learning ETH, Zurich    Switzerland
2. Analysis and Optimisation of Collaborative Machine Learning EURECOM France
3. Analysis and Synthesis of Machine Learning Algorithms in Large Dimensional Settings CTTC Spain
4. Multipoint channel charting for wireless channel prediction Aalto University Finland
5. Collaborative Deep Learning EURECOM France
6. Predictive Machine Learning for multiuser beamforming in massive MIMO Aalborg University Denmark
7. Machine Learning for real-time radio signal processing Ericsson AB Sweden
8. Machine Learning for Massive Connectivity CTTC Spain
9. Optimizing URLLC metadata/data flows using machine learning Aalborg University Denmark
10. Resource orchestration in large industrial wireless networks World Sensing Spain
11. Deep Reinforcement Learning for Radio Resource Management Nokia Bell Labs France
12. Anticipatory techniques for wireless network optimisation University of Padova (UNIPD) Italy
13. Cognition-Based Networks University of Padova (UNIPD) Italy
14. Context aware and anticipatory AI for next generation network parameter optimization Aalto University Finland
15. Data-driven end-to-end optimization of industrial wireless networks and applications Bosch Germany