LeadAIforDesignandControl

Experience in defining the technical requirements and expectations of AI solutions for engineering applications, Deep understanding of Machine Learning and AI algorithms for regression, classification and anomaly detection tasks applied to tabular data with uncertainty quantification and explainability requirements, Proven experience of applying AI algorithms for dynamical data sources (time series) regression, clustering, anomaly detection and uncertainty quantification, Experience in combining models with data from different sources and hybrid modelling approaches, Experience in defining models for 3D/Graph data types, Proven experience with optimization algorithms (e. g. gradient based, model based, non-parametric for parametric and non-parametric inputs) and active learning, Experience with traditional control algorithms calibration, MPC and RL, Experience in software programming languages including Python, C++, Working knowledge of modern agentic frameworks requirements, Proven track record of keeping up-to-date with industry trends and technology developments in the field of AI applied to Engineering, Working knowledge of model deployment techniques and edge serving, Working knowledge of turbomachinery thermodynamics, chemistry, rotor dynamic, Working knowledge in databases, Test Driven Development and Agile Development methods