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)

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)

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)

De Maio, V., Bermbach, D., & Brandic, I. (2023). TAROT: Spatio-Temporal Function Placement for Serverless Smart City Applications. In 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC) (pp. 21–30). IEEE. https://doi.org/10.1109/UCC56403.2022.00013 ( reposiTUm)

Cranganore, S. S., De Maio, V., Brandic, I., Do, T. M. A., & Deelman, E. (2022). Molecular Dynamics Workflow Decomposition for Hybrid Classic/Quantum Systems. In 2022 IEEE 18th International Conference on e-Science (e-Science) (pp. 346–356). IEEE. https://doi.org/10.1109/eScience55777.2022.00048 ( reposiTUm)

Zilic, J., De Maio, V., Aral, A., & Brandic, I. (2022). Edge offloading for microservice architectures. In Proceedings of the 5th International Workshop on Edge Systems, Analytics and Networking (pp. 1–6). Association for Computing Machinery. https://doi.org/10.1145/3517206.3526266 ( reposiTUm)

De Maio, V., Aral, A., & Brandic, I. (2022). A Roadmap To Post-Moore Era for Distributed Systems. In ApPLIED ’22: Proceedings of the 2022 Workshop on Advanced tools, programming languages, and PLatforms for Implementing and Evaluating algorithms for Distributed systems (pp. 30–34). Association for Computing Machinery (ACM). https://doi.org/10.1145/3524053.3542747 ( reposiTUm)

Tocze, K., Schmitt, N., Kargén, U., Aral, A., & Brandic, I. (2022). Edge Workload Trace Gathering and Analysis for Benchmarking. In 2022 IEEE 6th International Conference on Fog and Edge Computing (ICFEC) (pp. 34–41). IEEE. https://doi.org/10.1109/ICFEC54809.2022.00012 ( 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)

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)

Zhou, H., Aral, A., Brandic, I., & Erol-Kantarci, M. (2021). Multi-agent Bayesian Deep Reinforcement Learning for Microgrid Energy Management under Communication Failures. arXiv. https://doi.org/10.48550/arXiv.2111.11868 ( reposiTUm)