Summary
Jonas Huber is a data scientist and ML engineer with a PhD and nine years of experience turning complex signals and experimental data into production-ready machine learning solutions. He combines deep expertise in statistical modelling, time-series analysis, and deep learning (PyTorch/TensorFlow/XGBoost) with hands-on sensor-fusion and computer-vision systems built for real-time inference. Jonas has shipped end-to-end pipelines and reduced computation time through automation, contributed to regulated-device workflows (supporting FDA submissions), and applied Kalman filtering and robust outlier detection in safety-critical contexts. Comfortable across Python, R, and SQL, he pairs technical rigour with teaching and leadership—supervising students, delivering MSc modules, and presenting to multidisciplinary audiences. Based in London, he uniquely bridges cognitive neuroscience research (EEG and pediatric auditory work) and industrial AI deployments, bringing a researcher’s attention to signal fidelity into pragmatic product engineering.
9 years of coding experience
9 years of employment as a software developer
Bachelor’s Degree Psychology, Bachelor’s Degree Psychology at University of Aberdeen
Exchange Programme, Exchange Programme at City University of Hong Kong
University College London
German, Portuguese, English, Spanish, Latin