Job offer

Applications must be submitted exclusively through the official GENOME project website. Applications sent via LinkedIn, the Telefónica I+D website, email, or any other platform will not be considered. To ensure your candidacy is valid, you must follow the formal application process on the GENOME portal.

Apply here: https://jobs.genome-msca.eu/

The GENOME Doctoral Network at Telefónica Innovación Digital (TID) is recruiting a doctoral candidate 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, with a target mean time to detect below 0.1 s.

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.

What You Will Do (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 D1.1, D1.2, D4.1, D4.2 and D4.3 in collaboration with GENOME partners., The position includes a planned 5-month secondment at NEC Laboratories Europe, Germany (months 25-29 of the PhD, supervisor: Dr. A. Garcia-Saavedra). 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 virtualized 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.

Languages ENGLISH

Level Excellent

Research Field Computer science

• English (C1/C2 Proficiency), Research Field Computer science

Education Level Master Degree or equivalent, * 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., * 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 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., We are looking for candidates who combine a strong theoretical grounding in AI and machine learning with hands-on engineering and experimentation. The ideal profile is a researcher who has worked on efficient or novel neural architectures, LLMs or transformer-based systems, and who is eager to apply these skills to AI resilience, explainability, and secure automation in telecom environments. Profiles with experience in cybersecurity-oriented AI, federated or distributed learning, long-context modelling, or efficient model design are particularly attractive. We especially welcome applicants with evidence of research maturity through publications, research-engineer or research-fellow experience, and the ability to work independently on ambitious research problems. Candidates with a profile similar to the attached example CV, such as strong PyTorch/Python skills, efficient neural-network research, and publication activity, should be considered especially strong

Recruited fellows will receive a competitive salary, high-quality training and several other benefits, in full compliance with the Work Programme 2024-2025 for MSCA-DN Actions. Eligibility criteria

MSCA DN Eligibility Rules apply: https://marie-sklodowska-curie-actions.ec.europa.eu/actions/doctoral-networks Additional comments

Where You Will Work

• Host institution: Telefónica I+D (TID), Spain.
• Doctoral enrolment: UPC.
• Planned secondment: NEC Laboratories Europe, Germany, 5 months.
• Research duration: 36 months, subject to project and doctoral-programme conditions.