Telefónica Scientific Research
Telefónica Scientific Research is a leading industrial research lab, based in Barcelona (Spain). The aim of the lab is to carry out disruptive research that addresses several technological areas of interest to the Telefónica Group. The Scientific Research group follows an open research model in collaboration with universities and other research institutions, promoting the dissemination of their work both through publications and technology transfers. The board is constituted of a multi-disciplinary team with a variety of skills, including Artificial Intelligence, Networks, Cybersecurity and Privacy, AI Ethics and Neuroscience. We are seeking candidates at all levels of seniority for staff researcher positions, to strengthen our efforts in the respective 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", with PhD enrolment at the Universitat Politècnica de Catalunya (UPC). The project sits at the intersection of AI resilience, large language models, explainability, human-in-the-loop methods, and network automation for AI-native 6G and O-RAN systems. The selected researcher will develop context-aware mechanisms that identify and mitigate risks in AI-enabled network environments. The doctoral candidate will combine Human-in-the-Loop (HITL) methods, evolutionary optimisation, prompt and context engineering, long-context processing strategies, explainable AI, and multi-objective deep reinforcement learning to design resilient AI mechanisms for operator-grade environments. The work will balance 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 5-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 and help 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, with emphasis on securing AI-based network functions.
Produce high-quality scientific publications and contribute to project deliverables in collaboration with GENOME partners.
Attend physical/online meetings in relation to the GENOME project, or other occasions.
Essential Requirements
Master's degree in Telecommunications, Computer Science/Engineering,



Artificial Intelligence, Cybersecurity, or a closely related field.
MSCA Mobility Rule: You must not have resided or carried out your 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 (for example PyTorch, Hugging Face, TensorFlow, or equivalent tooling).
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, virtualized 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 wor

📌 Industrial Phd Researcher For Msca Doctoral Networks (Laza)
🏢 Telefonica Global Solutions Usa
📍 Laza