Victor van Wymeersch

• Machine learning (ML) models for improving conversion rates of marketing campaigns • Deep learning models (residual modelling) for anomaly detection in photovoltaic plants to improve power production • Sofware development • Deep Reinforcement learning and Monte Carlo Tree Search agents for optimising operational aspects of garbage collection vehicles within cities • Unsupervised clustering of heat pump timeseries faults

Toyota Engineer working on Research and Development of hybrid, electric, and fuel-cell powertrains: • Development and deployment of Natural Language Processing (NLP) based tools and pipelines • Data Science and Machine Learning • Building of data analytics dashboards and statistical forecasting • Strategic technical target setting • Optimisation-based control systems and mechatronic system modelling • Fuel cell and electric powertrain sizing and design • Project management

(Completed in my free time while working) Grade: Cum Laude Thesis: Deep Reinforcement Learning for autonomous vehicles in dense traffic. Design of neural network architectures to deal with dynamically sized and temporal sensor-based input data.

Grade: Cum Laude POC Member - Education Commission for Mechanical Engineering Sciences Thesis: A Natural Human-Robot Handover using On-line Optimal Control and Learning by Demonstration.

Grade: Upper second class (2.1) Research Thesis: • Quantitative comparison of computer-aided residual stress modeling techniques for laser shock peened aircraft aluminium. Design Thesis: • Design and optimization of organic Rankine cycle to utilize previously wasted latent heat.