Short CV
| 2024 – present | PhD candidate | Vrije Universiteit Amsterdam |
| 2023 – 2024 | Research assistant | Amsterdam UMC |
| 2020 – 2023 | Msc Computational Science | Vrije Universiteit Amsterdam & Universiteit van Amsterdam (joint degree) |
| 2020 – 2023 | Research assistant | Vrije Universiteit Amsterdam |
| 2017 – 2020 | BSc Computer Science | Vrije Universiteit Amsterdam |
Research
Computational modeling of human hippocampal and cortical neurons to investigate how their biophysical properties relate to memory function.
Highlighted publications
Weiler, R., Brucklacher, M., Pennartz, C., & Bohté, S. M. (2024). Masked Image Modeling as a Framework for Self-Supervised Learning across Eye Movements. arXiv preprint arXiv:2404.08526.
Weiler, R., Diachenko, M., Juarez-Martinez, E. L., Avramiea, A. E., Bloem, P., & Linkenkaer-Hansen, K. (2023). Robin’s viewer: using deep-learning predictions to assist EEG annotation. Frontiers in Neuroinformatics, 16, 1025847.
Diachenko, M., Houtman, S. J., Juarez-Martinez, E. L., Ramautar, J. R., Weiler, R., Mansvelder, H. D., … & Linkenkaer-Hansen, K. (2022). Improved manual annotation of EEG signals through convolutional neural network guidance. Eneuro, 9(5).
Capel, H., Weiler, R., Dijkstra, M., Vleugels, R., Bloem, P., & Feenstra, K. A. (2022). ProteinGLUE multi-task benchmark suite for self-supervised protein modeling. Scientific Reports, 12(1), 16047.