Twin‑in‑the‑loop (TiL) applications embed a high‑fidelity digital twin within control and estimation loops to enhance system performance and reliability. In this seminar, we introduce the TiL architectures and focus on the TiL filter - a state observer that employs a high-fidelity Digital Twin as prediction model. To tune this filter in complex black‑box settings, we develop methods to overcome the curse of dimensionality inherent in zeroth‑order optimization . We propose two complementary methods: projecting the optimization problem into a lower‑dimensional subspace of the most influential parameters and employing Gradient‑Informed Bayesian Optimization (GIBO) to conduct efficient, local black‑box optimization. Case studies demonstrate accelerated convergence and improved filter performance in a vehicular application.