Al-Dulaimy, A., Jansen, M., Johansson, B., Trivedi, A., Iosup, A., Ashjaei, M., Galletta, A., Kimovski, D., Prodan, R., Tserpes, K., Kousiouris, G., Giannakos, C., Brandic, I., Ali, N., Bondi, A. B., & Papadopoulos, A. V. (2024). The computing continuum: From IoT to the cloud. Internet of Things, 27, Article 101272. https://doi.org/10.1016/j.iot.2024.101272 ( reposiTUm)

De Maio, V., Kanatbekova, M., Zilk, F., Friis, N., Guggemos, T., & Brandic, I. (2024). Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing. In 2024 IEEE 24th International Symposium on Cluster, Cloud and Internet Computing (CCGrid) (pp. 626–635). https://doi.org/10.1109/CCGrid59990.2024.00075 ( reposiTUm)

Ilager, S., Balouek, D., Kaddour, S. M., & Brandic, I. (2024). Proteus: Towards Intent-driven Automated Resource Management Framework for Edge Sensor Nodes. In FlexScience’24 : Proceedings of the 14th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures (pp. 1–8). Association for Computing Machinery. https://doi.org/10.1145/3659995.3660037 ( reposiTUm)

De Maio, V., Bork, D., & Brandic, I. (2024). RIGOLETTO: A Workflow Definition Language for Hybrid Quantum-Classical Scientific Applications. In 2024 26th International Conference on Business Informatics (CBI) (pp. 40–49). https://doi.org/10.1109/CBI62504.2024.00015 ( reposiTUm)

Herbst, S., De Maio, V., & Brandic, I. (2024). Streaming IoT Data and the Quantum Edge: A Classic/Quantum Machine Learning Use Case. In Euro-Par 2023: Parallel Processing Workshops : Euro-Par 2023 International Workshops Limassol, Cyprus, August 28 – September 1, 2023 Revised Selected Papers, Part I (pp. 177–188). Springer. https://doi.org/10.1007/978-3-031-50684-0_14 ( reposiTUm)

Catalfamo, A., Aral, A., Brandic, I., Deelman, E., & Villari, M. (2024). Machine Learning Workflows in the Computing Continuum for Environmental Monitoring. In Computational Science – ICCS 2024 : 24th International Conference, Malaga, Spain, July 2–4, 2024, Proceedings, Part V (pp. 368–382). Springer. https://doi.org/10.1007/978-3-031-63775-9_27 ( reposiTUm)

Maliakel, P. J., Ilager, S., & Brandic, I. (2024). FLIGAN: Enhancing Federated Learning with Incomplete Data using GAN. In EdgeSys ’24: Proceedings of the 7th International Workshop on Edge Systems, Analytics and Networking (pp. 1–6). Association for Computing Machinery. https://doi.org/10.1145/3642968.3654813 ( reposiTUm)

Tundo, A., Mobilio, M., Ilager, S. S., Brandic, I., Bartocci, E., & Mariani, L. (2023). An Energy-Aware Approach to Design Self-Adaptive AI-based Applications on the Edge. In 2023 38th IEEE/ACM International Conference on Automated Software Engineering (ASE) (pp. 281–293). IEEE. https://doi.org/10.1109/ASE56229.2023.00046 ( reposiTUm)

Brandic, I. (2024). Challenges in the Design of Hybrid Classic-Quantum Systems [Keynote Presentation]. The 32nd Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP 2024), Dublin, Ireland. http://hdl.handle.net/20.500.12708/210997 ( reposiTUm)

Zilk, F., Tundo, A., De Maio, V., & Brandic, I. (2025). Breaking Down Quantum Compilation: Profiling and Identifying Costly Passes. arXiv. https://doi.org/10.48550/arXiv.2504.15141 ( reposiTUm)

De Maio, V., Kanatbekova, M., Zilk, F., Friis, N., Guggemos, T., & Brandic, I. (2024). Training Computer Scientists for the Challenges of Hybrid Quantum-Classical Computing. arXiv. https://doi.org/10.48550/arXiv.2403.00885 ( reposiTUm)

Herbst, S., De Maio, V., & Brandic, I. (2024). On Optimizing Hyperparameters for Quantum Neural Networks. arXiv. https://doi.org/10.48550/arXiv.2403.18579 ( reposiTUm)

Cranganore, S. S., De Maio, V., Brandic, I., & Deelman, E. (2024). Paving the Way to Hybrid Quantum-Classical Scientific Workflows. arXiv. https://doi.org/10.48550/arXiv.2404.10389 ( reposiTUm)

Chu, X., Hofstätter, D., Ilager, S. S., Talluri, S., Kampert, D., Podareanu, D., Duplyakin, D., Brandic, I., & Iosup, A. (2024). Generic and ML Workloads in an HPC Datacenter: Node Energy, Job Failures, and Node-Job Analysis. arXiv. https://doi.org/10.48550/arXiv.2409.08949 ( reposiTUm)

Herbst, S., Cranganore, S. S., De Maio, V., & Brandic, I. (2024). Exploring Channel Distinguishability in Local Neighborhoods of the Model Space in Quantum Neural Networks. arXiv. https://doi.org/10.48550/arXiv.2410.09470 ( reposiTUm)

Kanatbekova, M., Ilager, S. S., & Brandic, I. (2024). ABBA-VSM: Time Series Classification using Symbolic Representation on the Edge. arXiv. https://doi.org/10.48550/arXiv.2410.10285 ( reposiTUm)