PhD position in operational AI for production-grade mobile networks

Networks Data Science Group

Deadline for receipt of applications: December 1st, 2024 23:59 AoE (02 December 2024, 13:59h Europe/Madrid Time)

The Networks Data Science group (http://nds.networks.imdea.org/) at IMDEA Networks Institute has an opening for one PhD student in the area of mobile network intelligence. The successful candidate will design original AI solutions for the automation of network functionalities, perpetuating the recent history of breakthroughs achieved by the group in the development of AI for anticipatory mobile network operation [1-9]. The focus of the studies will be on surpassing a number of limitations that affect today’s AI paradigms and hinder their adoption
in operational industry-grade mobile network infrastructures. To this end, the candidate will contribute to devise a range of innovative AI models, implement them in emerging network architectures, and demonstrate their viability in production systems under real-world network deployments and traffic loads.

The position offers:

• hands-on training in applied AI for ICT
• a unique opportunity to work with large-scale real-world measurement data
• a vibrant, collaborative, multi-cultural and English-speaking environment
• the prospect to publish at top-tier venues in networking
• an advantageous path to a successful career in industry or academia [10]
• the high quality of life of the region of Madrid, Spain, where we are based.

The position requires:

• a degree in Computer Science or related field, with a solid academic record
• excellent programming skills
• a strong interest in applied AI (prior experience is a plus)
• fluency in written and spoken English
• enthusiasm for interdisciplinary research.

Inquiries on the position can be directed to the thesis supervisor, Dr. Marco Fiore, via email at marco.fiore@imdea.org.

Candidates shall submit by the call deadline a CV, a motivation letter, and the contact details of two references through the IMDEA Networks Institute hiring portal, at https://careers.networks.imdea.org/.

This position could be co-financed by the project “6G-IRONWARE: Predicción de tráfico de red móvil robusta en el tiempo para el 6G” (grant CNS2023-143870), funded by MICIU/AEI /10.13039/501100011033 and by the European Union NextGenerationEU/PRTR.

This position could be co-financed by the project “ORIGAMI: Optimized resource integration and global architecture for mobile infrastructure for 6G” (Grant Agreement n. 101139270), funded by the Smart Networks and Services Joint Undertaking (SNS JU) and the European Commission through Horizon Europe program call HORIZON-JU-SNS-2023.

[1] L. Schiavo, G. Garcia-Aviles, A. Saavedra, M. Gramaglia, M. Fiore, A. Banchs, X. Costa-Perez, CloudRIC: Open Radio Access Network (O-RAN) Virtualization with Shared Heterogeneous Computing ACM MobiCom 2024
[2] A. Collet, A. Bazco-Nogueras, A. Banchs, M. Fiore, Explainable and Transferable Loss Meta-Learning for Zero-Touch Anticipatory Network, Management IEEE Transactions on Network and Service Management, 21:3, 2024
[3] C. Fiandrino, E. Pérez-Gómez, P. Fernández-Pérez, H. Mohammadalizadeh, M. Fiore, J. Widmer, AIChronoLens: Advancing Explainability for Time Series AI Forecasting in Mobile Networks, IEEE INFOCOM 2024
[4] S. Alcala-Marin, A. Bazco-Nogueras, A. Banchs, M. Fiore, kaNSaaS: Combining Deep Learning and Optimization for Practical Overbooking of Network Slices, ACM MobiHoc 2023
[5] A. Collet, A. Bazco Nogueras, A. Banchs, M. Fiore, AutoManager: a Meta-Learning Model for Network Management from Intertwined Forecasts, IEEE INFOCOM 2023
[6] A. Collet, A. Banchs, M. Fiore, LossLeaP: Learning to Predict for Intent-Based Networking, IEEE INFOCOM 2022
[7] C. Zhang, M. Fiore, I. Murray, P. Patras, CloudLSTM: A Recurrent Neural Model for Spatiotemporal Point-cloud Stream Forecasting, AAAI 2021
[8] D. Bega, M. Gramaglia, M. Fiore, A. Banchs, X. Costa-Perez, AZTEC: Anticipatory Capacity Allocation for Zero-Touch Network Slicing, IEEE INFOCOM 2020
[9] D. Bega, M. Gramaglia, M. Fiore, A. Banchs, X. Costa-Perez, DeepCog: Cognitive Network Management in Sliced 5G Networks with Deep Learning, IEEE INFOCOM 2019
[10] https://networks.imdea.org/team/imdea-networks-team/alumni-network/

Equal Employment Opportunity

IMDEA Networks Institute aims to increase the proportion of women and therefore qualified female applicants are explicitly encouraged to apply. Until a balanced ratio of men and women has been achieved at the institute, preference will be given to women if applicants have similar qualifications. IMDEA Networks Institute actively promotes diversity and equal opportunities. Applicants are not to be discriminated against in personnel selection procedures on the grounds of gender, ethnicity, religion or ideology, age, sexual orientation (anti-discrimination). People with disabilities who have the relevant qualifications are expressly invited to apply.