This page outlines defended dissertations within APROPOS. The ones with dates were publicly defended while others still have the initial topics listed.
- ESR1 (UPV, PhD, 13.02.2025): Efficient Mixed-Precision Inference for Vision Transformers
- ESR2 (UVA, PhD, 02.2026): Tentative title: Reliability-Aware Energy-Efficient Scheduling Techniques for Many-Core Processors
- ESR3 (UNIBO, PhD, 01.2026): Machine Learning for Approximate Computing
- ESR4 (TUD, PhD, 09.2025): Adaptive Energy-aware Framework for Connected Vehicle Services: Approximate Computing for Vehicular Edge AI
- ESR5 (KTH, PhD, 10.2026): Design Space Exploration and Approximate Computing for Machine Learning and DSP Applications
- ESR6 (ECL-INL, PhD, 17.12.2024): Design space exploration for accuracy-aware computing
- ESR7 (POLITO, PhD, 05.2025): Fast and Accurate Prediction of the Impact of Approximate Operators on a Complex Computation
- ESR8 (POLIMI, PhD, 23.04.2025): Construction of Automatic Precision Tuning Tools
- ESR9 (UPV, PhD, 15.10.2024): Deep Learning Inference on Low-Power Commodity Processors and the AMD Versal AI Engine
- ESR10 (TAU, D.Sc.(Tech.), 19.12.2024): Recofigurable Approximating Accelerators for Edge Computing
- ESR11 (TAU, D.Sc.(Tech.), 21.02.2025): Approximating Techniques for Low-Power GNSS Receivers
- ESR12 (UTU, D.Sc.(Tech.), 02.2026): DNN and Transformers Co-Scheduling on Distributed Edge Platforms
- ESR13 (TUW, Dr. Techn., 09.2025): Approximate Computing for Deep Learning at the Edge: Enhancing Energy Efficiency While Maintaining Accuracy
- ESR14 (TAU, D.Sc.(Tech.), 27.09.2024): Direction-of-Arrival-based Indoor Localization Systems for Massive IoT Networks
- ESR15 (QUB, PhD, 05.2025): Workload-aware timing error prediction and mitigation via lightweight neural networks and algorithm-architecture co-design