Professor of Applied Artificial Intelligence & Data Science
HAW Hamburg
Since August 2026, I am Professor of Applied Artificial Intelligence and Data Science at Hamburg University of Applied Sciences (HAW Hamburg).
My research focuses on applied machine learning and artificial intelligence, with a particular interest in methods for complex biomedical and scientific data.
At HAW Hamburg, I combine research in these areas with teaching e.g. in artificial intelligence, machine learning, data science and statistics.
Professor of Data Science
NORDAKADEMIE Elmshorn
From 2023 to 2026, I was Professor of Data Science at NORDAKADEMIE. I taught artificial intelligence, machine learning, data science, statistics, mathematics, and software engineering across bachelor’s and master’s programs, and supervised numerous student projects and theses.
Alongside teaching, I worked on research projects in applied AI and data science, particularly in medical imaging, large language models, and scientific AI applications. I initiated research collaborations with academic and industry partners, contributed to scientific publications and research proposals, and was involved in the university’s work on AI strategy and infrastructure.
From 2025, I also led the design and development of the new Master’s program in Applied Artificial Intelligence, including its curriculum and accreditation process.
(Senior) Research Scientist
Philips Research Hamburg
From 2015 to 2023 I worked on AI-driven solutions for MRI, CT and ultrasound imaging. My contributions supported clinical workflows and product development across several modalities. I collaborated with international research groups, developed deep-learning tooling, and contributed to agile program structures as Scrum Master and Release Train Engineer.
Research in ML & Neuroimaging
UKE • NIRx • Fraunhofer FIRST
Earlier in my career I worked in academic and applied research on EEG/MEG & NIRS data analysis, functional connectivity, neuroimaging, signal processing, and multimodal data integration. This work shaped my expertise in combining statistical methods, machine learning, and complex biomedical data analysis.
Academic Background
TU Berlin • TU Hamburg
I hold a doctoral degree (Dr. rer. nat., summa cum laude) in machine learning and signal processing from TU Berlin, supervised by Dr. Guido Nolte and Prof. Dr. Klaus-Robert Müller. My dissertation focused on multivariate EEG/MEG analysis and brain connectivity:
http://files.aewald.net/PhDThesis_ArneEwald.pdf
I studied Informatik-Ingenieurwesen (Dipl.-Ing.) and General Engineering Science (B.Sc.) at TU Hamburg.