Scholar
Rafael Bischof
Google Scholar ID: MKu0A6sAAAAJ
Ph.D. Student, ETH Zurich
Citations & Impact
Citations
321
H-index
6
i10-index
3
Publications
17
Co-authors
17
list available
Publications
Resume (English only)
Academic Achievements
- - HyPINO: Multi-Physics Neural Operators via HyperPINNs and the Method of Manufactured Solutions, Sep 2025
- - AIXD: AI-eXtended Design Toolbox for data-driven and inverse design, Sep 2025
- - Heritage++, a Spatial Computing approach to Heritage Conservation, Jan 2025
Research Experience
- - Automation Center - PostFinance, DevOps Engineer, September 2023 – February 2024
- - IBM Research, Internship as Machine Learning Engineer, March 2023 – February 2024
- - Swiss Data Science Center, Data Scientist, April 2022 – February 2023
- - Research in Orthopedic Computer Science - Balgrist University Hospital, Research Assistant, December 2021 – April 2022
- - Institute of Structural Engineering - ETHZ, Research Assistant, August 2021 – December 2021
Education
- - Computational Design Lab - ETHZ, Doctoral Candidate
- - Eidgenössische Technische Hochschule Zürich - ETHZ, M.Sc. in Computer Science with focus on Information Systems
- - École Polytechnique Fédérale de Lausanne - EPFL, B.Sc. in Computer Science
- - Gymnasium Biel-Seeland, Bilingual Maturität German / French with focus on Physics and Applied Mathematics
Background
- As a Ph.D. student in the Computational Design Lab at ETH Zurich, my research is focused on utilizing machine learning techniques to tackle engineering challenges by incorporating prior domain knowledge in the form of inductive biases. This results in improved model accuracy and robustness, especially when dealing with limited data in various forms, such as tabular, temporal, image, and graph data. I am particularly interested in physics-informed neural networks, which utilize physical laws and constraints to guide the model’s predictions and enhance performance.
