Computing Feynman integrals remains a central bottleneck for high-precision scattering-amplitude predictions. The differential-equation method has proven crucial to overcome this challenge, providing analytic solutions for multi-loop integrals of simple processes. For massive, multi-leg channels, however, practical limitations arise. In this talk, I will give a brief overview of the method, to then shift the focus to its current limitations. Then, I will introduce a new integrator that overcomes some of these challenges, enabling fast and precise evaluation of cutting-edge Feynman integrals. This talk is based on [2507.12548].

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The seminar will provide a glimpse of some elements of the rapidly evolving field of quantum sensing, with a particular focus on applications in particle physics. Specific approaches involving quantum systems, such as low-dimensional systems or manipulations of ensembles of quantum systems, hold great promise for improving high-energy particle physics detectors, particularly in areas like calorimetry, tracking, and timing. The use of quantum sensors for high-precision measurements, such as precision spectroscopy of novel atomic, molecular or ionic systems, as well as the development of new quantum sensors based on superconducting circuits, ion and particle traps, crystals, and nanomaterials, are equally relevant for low energy measurements that rely on high energy physics infrastructures. Significant advances and improvements in existing or future quantum technologies will be necessary to address such topics related to the dark universe, the detection of relic neutrinos, precision tests of symmetries and of the standard model and probing general foundational issues in physics. The seminar will thus also feature discussions of the Quantum Sensing Initiatives at CERN and the ECFA R&D Roadmap on Quantum Sensing and Advanced Technologies and will discuss options for future collaborations in the context of the recently approved DRD5 implementation of the roadmap.

Biomolecules are incredibly dynamic, constantly shifting between various conformations within a network connected by infrequent structural intermediates. This collection of structures, known as their conformational ensemble, including these rare intermediate structures, dictates how biomolecules function within a cell. However, comprehensively mapping these ensembles remains a significant challenge for both computational and experimental methods. Computer simulations, enhanced by machine learning, offer a promising solution to these challenges in biomolecular sciences. In the first part of my talk, I'll showcase our work on integrating path sampling with machine learning. This empowers us to simulate rare conformational transitions more effectively. Our algorithm provides efficient sampling and delivers crucial mechanistic, thermodynamic, and kinetic insights into these rare molecular events, all at a moderate computational cost. The second part of my talk will focus on using simulation-based inference to identify biomolecular conformations in cryo-electron microscopy (cryo-EM) data. Cryo-EM is a powerful tool for characterizing protein conformational ensembles. Even though a frozen sample contains information about the entire ensemble, accurately identifying rare or disordered molecular conformations from a single cryo-EM image is still difficult. To address this, we developed the cryo-EM simulation-based inference (cryoSBI) framework by integrating physics-based simulations, Bayesian inference, and deep learning. This framework allows us to infer molecular conformations and their associated uncertainties directly from individual cryo-EM images. We've validated cryoSBI using both synthetic and experimental data. This approach opens new avenues for characterizing entire conformational ensembles using experimental data.