Vicomtech (www.vicomtech.org) is a private Applied Research Centre specializing in Artificial Intelligence, Visual Computing, and Interaction, with locations in Donostia-San Sebastián and Bilbao. It holds a strong national presence in applying cutting-edge AI technologies across multiple sectors. We are members of the Basque Research and Technology Alliance (BRTA) and GraphicsVision.ai, an international network specialized in Computer Graphics (https://graphicsvision.ai/).

We are looking for a Predictive Modelling and AI Engineer with a scientific-technical profile and solid software engineering skills, motivated by solving complex industrial problems by combining physical knowledge of systems with machine learning: predicting the behavior of assets and processes, quantifying associated uncertainty, and transferring these models to production.

Responsibilities:

• Design and develop predictive models applied to system diagnostics and prognostics: Remaining Useful Life (RUL) estimation, degradation and fault detection/classification, multivariate time series forecasting, and asset health models.
• Develop hybrid models integrating physical knowledge and data, with a special focus on Physics-Informed Neural Networks (PINNs) and other scientific machine learning approaches.
• Apply uncertainty quantification techniques (approximate Bayesian, ensembles, conformal prediction, uncertainty propagation) so that predictions reach the end user with a usability confidence metric for decision-making.
• Build surrogate models (reduced order models, physical simulator emulators) to replace expensive simulations in optimization and control loops.
• Integrate these models into Model Predictive Control (MPC) and real-time optimization schemes in collaboration with domain and control teams.
• Take models beyond the notebook: packaging, deployment, monitoring, and retraining, contributing to our MLOps lifecycle.
• Contribute to the improvement and expansion of our software assets (internal libraries, modelling, and data platforms) by creating reusable capabilities.
• Collaborate with multidisciplinary engineering, research, and business teams, and participate in scientific publications and international conferences.