Overview
Telefónica Scientific Research is a leading industrial research lab based in Barcelona (Spain). The lab aims to carry out disruptive research in several technological areas of interest to the Telefónica Group, following an open research model in collaboration with universities and other research institutions. Its multidisciplinary board includes expertise in Artificial Intelligence, Networks, Cybersecurity & Privacy, AI Ethics, and Neuroscience. We are seeking candidates at all levels of seniority for staff researcher positions to strengthen our efforts across these areas.
Mission
The GENOME Doctoral Network at Telefónica Innovación Digital (TID) is recruiting a Doctoral Candidate (DC) for the TID‐1 research project "Context‐aware and evolutionary framework for AI resilience". The PhD enrolment will be at the Universitat Politècnica de Catalunya (UPC). The selected researcher will develop context‐aware mechanisms to identify and mitigate risks in AI‐enabled network environments, combining Human‐in‐the‐Loop methods, evolutionary optimisation, prompt and context engineering, long‐context processing strategies, explainable AI, and multi‐objective deep reinforcement learning. The work balances rigorous algorithmic research with practical validation on realistic telecom platforms, with a strong emphasis on scientific publications, consortium collaboration, and real‐world relevance, including a planned five‐month secondment at NEC Laboratories Europe in Germany.
Responsibilities
Explore Human‐in‐the‐Loop approaches that translate human language and operator intent into adaptive, security‐aware system requirements and network policies.
Design evolutionary and genetic optimisation methods for context embeddings and prompt adaptation in LLM‐based AI resilience pipelines.
Investigate large‐context processing strategies, including scalable memory and decomposition methods, to handle complex security and network‐management data.
Develop explainable AI mechanisms that integrate with transformer‐based models to address the performance‐versus‐explainability trade‐off.
Design and validate multi‐objective deep reinforcement learning architectures for identifying and mitigating risks in scenarios such as peer‐to‐peer and federated learning.
Evaluate the framework on operator‐oriented and O‐RAN / vRAN experimental environments, emphasising the security of AI‐based network functions.
Produce high‐quality scientific publications and contribute to project deliverables in collaboration with GENOME partners.
Attend physical/online meetings related to the GENOME project and other occasions.
Essential Requirements
Master's degree in Telecommunications, Computer Science/Engineering, Artificial Intelligence, Cybersecurity, or a closely related field.
MSCA Mobility Rule: No residence or main activity in Spain for more than 12 months in the last 3 years prior to the planned start date.
Academic excellence: eligibility to enrol in the UPC doctoral programme.
Fluency in English (C1 level or higher).
Hands‐on experience with Python and modern AI/ML frameworks (e.g., PyTorch, Hugging Face, TensorFlow or equivalent).
Solid foundation in machine learning, deep learning, transformers and/or neural network architectures.
Preferred Qualifications
Experience with large language models, transformers, prompt engineering, long‐context methods, or related generative‐AI pipelines.
Experience with AI security, trustworthy AI, adversarial robustness, explainability, or cybersecurity‐oriented machine learning.
Experience with reinforcement learning, multi‐objective optimisation, federated learning, or distributed AI systems.
Familiarity with efficient neural architectures, including alternative attention mechanisms, mixture‐of‐experts models, or resource‐aware model design.
Exposure to telecom, 5G/6G, O‐RAN, virtualised RAN, or AI‐native network‐management use cases.
Experience with real‐world data pipelines, experimental evaluation, and scalable model‐development workflows.
Evidence of research excellence through publications, preprints, open‐source contributions, research internships, or research‐engineer / research‐fellow roles.
Personal Qualities
Ability to bridge theoretical AI methods with practical resilience and security problems in operator‐grade network environments.
Motivation to work in an international, multidisciplinary doctoral network and collaborate across academia and industry.
High level of initiative, ownership, and persistence in long‐horizon research problems.
Willingness to publish in leading venues and communicate results clearly to both academic and industrial audiences.
Secondment
The position includes a planned five‐month secondment at NEC Laboratories Europe, Germany (months 25‐29 of the PhD). During the secondment you will:
* Evaluate the developed AI resilience framework on NEC's O‐RAN experimental platform using objective performance and robustness metrics.
* Investigate how to secure AI‐based functions in virtualised RAN environments and stress‐test the framework under realistic deployment conditions.
* Gain direct exposure to industrial experimentation and contribute to turning the research results into deployable resilience mechanisms.
Language Requirements
Language: English – Level: Excellent (minimum C1; C2 preferred).
What do we offer?
Work‐life balance measures and flexible hours.
Continuous training and certifications.
Hybrid telecommuting model.
Attractive social benefits package.
Excellent dynamic and multidisciplinary work environment.
Volunteering programs.
We are convinced that diverse and inclusive teams are more innovative, transformative, and achieve better results. We promote and guarantee inclusion of all people regardless of gender, age, sexual orientation and identity, culture, disability, or any other condition.
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