Organisation/Company Iquadrat Informatica SL Department Research&Development Research Field Engineering » Communication engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Application Deadline 30 Apr 2026 - 00:00 (Europe/Madrid) Country Spain Type of Contract Temporary Job Status Full-time Hours Per Week 40 Offer Starting Date 1 Mar 2026 Is the job funded through the EU Research Framework Programme? Horizon 2020 Is the Job related to staff position within a Research Infrastructure? No
Offer Description
IQUADRAT INFORMATICA S.L. (IQU) is a technology company founded in 1997, focused on delivering System-Level evaluation tools for wireless networks. IQU R&D has been providing global solutions and services in the use of the Internet and Telecommunications to companies and institutions that use information and communication technologies in commercial, marketing, management, and business communication areas. IQU’s R&D has a focused business plan specifically intended to deliver a “new generation of research tools for system-level Evaluation of wireless integrated communication systems. The R&D department is involved in many National and EU-funded research projects, and has extensive know-how in 5G/6G communication platforms, IoT platforms, and Demand Response algorithms for the Smart Grid. IQU is also active in many EU-funded research projects, related to Wireless Communications, 5G/6G, Smart Grids, and IoT.
IQU´s R&D department is seeking one doctoral candidate within the framework of the Marie Curie Doctoral Network NEWTON, funded by the European Commission.
You can find more information on this project at NEWTON, https://newton-6g.eu/. The Doctoral Network is led by IQUADRAT, the ATHINA-EREVNITIKO KENTRO KAINOTOMIAS STIS TECHNOLOGIES TIS PLIROFORIAS, TON EPIKOINONION KAI TIS GNOSIS (ISI/ATH), and comprises top European partners in the field, apart from IQU, which are KATHOLIEKE UNIVERSITEIT LEUVEN (KUL), ISRD SP Z O.O. (ISRD), EUR-EURECOM GIE, PANEPISTIMIO DYTIKIS ATTIKIS (UWA), NVIDIA DENMARK APS (NVIDIA), FUNDACIO PER A LA UNIVERSITAT OBERTA DE CATALUNYA (UOC), and RANPLAN WIRELESS NETWORK DESIGN LTD (RAN).
NEWTON is a newly launched Marie Skłodowska-Curie Doctoral Network that offers fifteen (15) PhD positions in European countries. NEWTON proposes a new 6G networking approach spanning the radio-edge, regional-edge, and network-management domains and aligned with major 5G/6G technology roadmaps worldwide: at the radio-edge, it introduces cell-free (CF) solutions based on O-RAN and AI to improve radio capacity while reducing energy consumption by up to 30% and cutting costs by a factor of 4 via distributed cascaded optical-hybrid processing, alongside neuromorphic schemes targeting an order-of-magnitude power reduction in multi-antenna installations, a 10 dB BER improvement, and a 3 dB increase in processing-unit noise tolerance; at the regional-edge, it advances fronthaul support for CF radio units using multi-band XL-MIMO beamforming to double spectral efficiency versus mMIMO by leveraging context and array geometry, develops scalable Cloud Edge Continuum interconnection of data centers to at least halve SLA-violation correction time and SLA violation levels, and defines a converged wireless–optical design using TWDM-PONs to boost both spectral and energy efficiency by 2× in uplink and 3× in downlink; finally, at the network-management level, it delivers AI/ML-driven, multi-objective, user-centric orchestration for CF-mMIMO to improve overall energy efficiency by 20%, a decentralized compute-orchestration framework improving prediction accuracy by at least 25%, and joint management of radio, optical, and compute resources in multi-tenant environments to increase utilization by up to 20% through smart slicing. In this context, NEWTON will build a training network of 15 Doctoral Candidates who will conduct top-notch research to develop and experimentally evaluate a range of techniques, methodological frameworks, and tools.
The candidate must have a master's degree in electrical, electronics, telecommunications, photonics engineering, or a similar degree with an academic level equivalent to the master's degree.
Specific Requirements
Please send your application before April 30 2026, via the Apply now button below, and include:
* Master and Bachelor Diplomas (single file)
* Master and Bachelor Grade Transcript (single file)
* Track record of research excellence
Languages ENGLISH – Level: Good
Research Field Engineering » Communication engineering
Additional Information
The Doctoral Candidates (DC) Fellowship is offered under very attractive employment conditions and competitive salaries in compliance with the MSCA Working Program. Salary is regulated by the EU total contributions that can reach 3833,56 € for the living allowance (country coefficient is applied), 710 € for the mobility allowance, and could be topped up with 660€ in case of family obligations. It should be highlighted that these amounts do not represent the gross salary but include all employer and employees’ taxes and contributions. The period of employment is 36 months. The DC will take part in a secondment to another project partner for up to 6 months of their appointment period. The candidate will be enrolled in a PhD degree program.
Eligibility criteria
The candidate can be of any nationality, but they should not have resided or carried out their main activity in the country of the host institution of the position they are applying to for more than 1 year in the past 3 years.
The candidate should not have already been awarded a PhD degree.
This position targets to develop a novel native integration of AI/ML in the new 6G RAN architecture that adds an extra level of coordination and conflict resolution between the different AI/ML models operating at the various elements of the NEWTON RAN system. The aim will be to simultaneously optimize 6G KPIs, which might be conflicting (e.g., energy consumption optimization versus performance). This entails generalizing AI/ML models to enable joint optimization of different RAN functions within or across protocol layers, in some cases, considering the timeframe of the specific RAN operations in the control loop and the features of CF networking. Thus, this position will develop a set of novel AI-based solutions for the optimization of the CF-based RAN system by applying a combination of model-based and data-driven approaches, aiming at maximizing the SE and EE. Distributed DRL approaches will be proposed, where multiple RL agents cooperate and converge into the optimal end-to-end network configuration given a set of overarching objectives and constraints (e.g., energy consumption, radiation exposure). These agents will orient their training and decisions to enable fast reconfiguration of the RAN components and migration of content and/or computational functions in a highly dynamic way, responding to changes in network state, UE location, or faults.
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