CV
Education
- Ph.D. in AI-based Prognosis of Dynamic Energy Systems, University of Oslo, 2023–2027 (expected)
- M.Sc. in Artificial Intelligence, University of Groningen, 2022 (8.4/10)
- B.Sc. in Psychology (Honours, Excellence Programme), University of Groningen, 2019
Research interests
- Probabilistic time-series forecasting; uncertainty quantification
- Deep lattice networks and monotonic / non-parametric CDF models
- Solar irradiance forecasting
Teaching experience
Publications
Estimating Lithium-Ion Battery State-of-Health with Monotonic Lattice Networks
Fordham, D., & Erdmann, N. (2026). "Estimating Lithium-Ion Battery State-of-Health with Monotonic Lattice Networks." SSRN preprint.
Deep Learning Multihorizon Irradiance Nowcasting: A Comparative Evaluation of Three Methods for Leveraging Sky Images
Eriksen, E. W., Nygård, M. M., Erdmann, N., & Riise, H. N. (2026). "Deep Learning Multihorizon Irradiance Nowcasting: A Comparative Evaluation of Three Methods for Leveraging Sky Images." IEEE Journal of Photovoltaics, 16(4), 594–604.
Multi-Horizon Time Series Forecasting of Non-Parametric CDFs with Deep Lattice Networks
Erdmann, N., Bentsen, L., Stenbro, R., Riise, H. N., Warakagoda, N. D., & Engelstad, P. E. (2026). "Multi-Horizon Time Series Forecasting of Non-Parametric CDFs with Deep Lattice Networks." Proceedings of the 40th AAAI Conference on Artificial Intelligence.
Reinforcement Learning for Pollution Detection in a Randomized, Sparse and Nonstationary Environment with an Autonomous Underwater Vehicle
Zieglmeier, S., Erdmann, N., & Warakagoda, N. D. (2025). "Reinforcement Learning for Pollution Detection in a Randomized, Sparse and Nonstationary Environment with an Autonomous Underwater Vehicle." arXiv preprint arXiv:2510.26347.
Deep and Probabilistic Solar Irradiance Forecast at the Arctic Circle
Erdmann, N., Bentsen, L. Ø., Stenbro, R., Riise, H. N., Warakagoda, N., & Engelstad, P. (2024). "Deep and Probabilistic Solar Irradiance Forecast at the Arctic Circle." Proceedings of the 2024 IEEE 52nd Photovoltaic Specialist Conference (PVSC), pp. 1599–1606.
Dynamic Modeling of P(VDF-TrFE-CTFE)-based Soft Actuators via Echo State Networks
D'Anniballe, R., Erdmann, N., Selleri, G., & Carloni, R. (2022). "Dynamic Modeling of P(VDF-TrFE-CTFE)-based Soft Actuators via Echo State Networks." Proceedings of the 2022 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), pp. 118–124.
Talks and posters
Multi-Horizon Time Series Forecasting of Non-Parametric CDFs with Deep Lattice Networks
Conference talk at 40th AAAI Conference on Artificial Intelligence, Singapore
Forecasting Uncertainty with ML
Invited talk at FME Solar Energy, Online
Deep and Probabilistic Solar Irradiance Forecast at the Arctic Circle
Poster at IEEE 52nd Photovoltaic Specialist Conference (PVSC), Seattle, WA, USA
Hierarchical Reinforcement Learning with AUVs
Talk at University of Oslo, Oslo, Norway
Awards
- Best Poster Award, IEEE 52nd Photovoltaic Specialist Conference (PVSC), 2024
- Best Paper Award, PhD educational component, University of Oslo
- Best Performer, Leukemia Challenge, University of Groningen
Service and leadership
- Representative for PhDs and postdocs, Department Board, Department of Technology Systems, University of Oslo (2025)
- Vice-representative for PhDs and postdocs, Faculty Board, Faculty of Mathematics and Natural Sciences, University of Oslo (2024)
Skills
- Programming: Python, C, R, Matlab
- ML: TensorFlow, PyTorch, SciPy, Optuna, OpenCV
- Tools: Git, LaTeX, Slurm
- Languages: German (native), English (professional), Dutch & Norwegian (elementary)