Researcher - Data & Machine Learning Engineering

and maintain scalable data pipelines (batch and streaming), integrating heterogeneous data sources such as IoT devices, APIs, and scientific datasets. - Develop, train, validate, and deploy machine learning models for tasks such as forecasting, optimisation, classification, and anomaly detection. - Implement end-to-end ML systems (MLOps), including data versioning, model training pipelines, CI/CD, and monitoring in production. - Architect and optimise data platforms (data lakes, warehouses) ensuring data quality, governance, and traceability. - Deploy and scale solutions in cloud environments using containerisation and microservices. - Contribute to the development and integration of AI-powered digital twins, combining physics-based models with data-driven surrogates. - Collaborate in R&D projects, contributing to state-of-the-art analysis, technical deliverables, and innovation strategies. - Work closely with multidisciplinary teams to translate complex problems into robust AI-driven solutions. - Participate in the preparation and writing of EU-funded research proposals (e.g., Horizon Europe), including the definition of technical work packages, pilot use cases, and consortium contributions. Why is IDENER.AI the place for you? - Our horizontal structure ensures you with the freedom to propose and execute solutions without bureaucratic barriers from day one. - We make work-life balance a central part of our culture by offering a fully flexible schedule, where you decide how to allocate your annual working time. - We strive to maintain a work environment that cares, supports, and inspires by focusing on every team member's financial, mental, physical, and emotional needs. - Our organisation is built on trust, cooperation, and equity, providing you with the safety and accountability required to propose and execute your own ideas from day one. - You will always have a senior team acting as a safety net to guide and support you while you

develop your full potential in the field of applied computational science. - BSc, MSc, or PhD in Computer Science, Data Science, Telecommunications, Mathematics, Physics, Electronics, Robotics and Mechatronics Engineering (GIERM), Aerospace Engineering, or related fields. - Professional proficiency in English is essential, as you will be responsible for drafting technical deliverables and engaging in high-level discussions within international consortiums. - Strong programming skills in Python (pandas, PySpark, scikit-learn, etc.) and advanced SQL. - Experience with machine learning frameworks (TensorFlow, PyTorch, XGBoost…). - Hands-on experience with data pipeline tools (Airflow, Prefect, Spark…). - Familiarity with cloud computing concepts and infrastructure. - Solid understanding of MLOps practices (Docker, CI/CD, model deployment). - Hands-on experience with AI coding assistants (e.g., Claude, GitHub Copilot) for development and research workflows.