Accurately predicting friction in sliding interfaces that contain third-body wear particles is critical for engineering applications such as sliding movement in pistons, bearings or metal forming. We present a hierarchical multiscale framework that links particle-scale mechanics to macroscopic friction in a strip-draw friction test. At the macroscale, a one-dimensional finite-element model reproduces the global stress state of the strip-draw setup and updates the local wear-particle density via Archard’s law. The local friction force at each node is then computed from mesoscale simulation results. At the mesoscale, a coupled discrete-element boundary-element approach resolves load sharing between rough surfaces and rigid oblate-spheroidal wear particles. The mesoscale solution returns to the macroscale solver a friction coefficient that depends on normal pressure, sliding velocity, surface geometry, and particle density, thereby closing the loop between scales. The simulated friction coefficient matches strip-draw experiments, capturing both the observed decrease in friction with increasing normal pressure and the influence of tool-pad size.

Stroke is a leading cause of long-term disability, frequently associated with persistent motor deficits. Gamma band oscillations, generated by synchronous discharge of parvalbumin-positive interneurons (PV-INs), are critically affected after stroke in humans and animals. Both gamma band and PV-INs play a key role in motor function, thus representing a promising target for poststroke neurorehabilitation. Noninvasive neuromodulatory approaches are considered a safe intervention and can be used for this purpose. Here, we present a novel, clinically relevant, noninvasive, and well-tolerated sub-acute treatment combining robotic rehabilitation with advanced neuromodulation techniques, validated in a mouse model of ischemic injury. During the sub-acute poststroke phase, we scored profound deficits in motor-related gamma band activity in the perilesional cortex. These deficits were accompanied by reduced PV-IN firing rates and increased functional connectivity, both at the perilesional and at the whole-cortex levels. Therefore, we tested the therapeutic potential of coupling robotic rehabilitation with optogenetic PV-IN-driven gamma band stimulation in a subacute poststroke phase during motor training to reinforce the efficacy of the treatment. Frequency-specific movement-related gamma band stimulation, when combined with physical training, significantly improved forelimb motor function. More importantly, by pairing robotic rehabilitation with a clinical-like noninvasive 40 Hz transcranial Alternating Current Stimulation, we achieved similar motor improvements mediated by the effective restoring of movement-related gamma band power, improvement of PV-IN maladaptive network dynamics, and increased PV-IN connections in premotor cortex. Our research introduces a new understanding of the role of parvalbumin-interneurons in poststroke impairment and recovery. These results highlight the synergistic potential of combining perilesional gamma band stimulation with robotic rehabilitation as a promising and realistic therapeutic approach for stroke patients.

We present findings from radar calibration experiments involving three radars operated by the Colorado State University (CSU) in the US and by the École Polytechnique Fédérale de Lausanne (EPFL) in Switzerland. The experiments were based on the comparison between measured radar variables and the known properties of artificial point targets electronically generated with a polarimetric radar target simulator (RTS) from Palindrome Remote Sensing. Radars under test included the two magnetron-based radars CHILL and SPLASH (its mobile version) from CSU and EPFL's new solid-state radar StXPol. For the CHILL and SPLASH calibration measurements in Colorado, a mobile lifting platform was employed that elevated the target simulator instrument to approximately 15 m above ground. The creation of virtual targets with polarimetric signatures allowed for a direct calibration of polarimetric variables. While the SPLASH radar exhibited good Zdr and sufficient Zh accuracy, remarkable precision and stability were found in CHILL's reflectivity data time series, where the reflectivity bias compared to the virtual target was less than 0.2 dB over a 1 h time series. Calibration issues that arise with solid-state radar systems were investigated with experiments conducted with the EPFL StXPol radar. This pulse compression system transmits a linear frequency-modulated long pulse as well as a non-modulated short pulse for observations at close ranges. The two pulses are separated in frequency by 50 MHz, and consequently calibration targets were generated independently for the two channels. Excellent stability and accuracy were found for Zdr in both channels. While Zh stability was also very high, a large reflectivity bias in both the long and the short pulse channel was detected. For the first time, the article introduces and analyzes a weather radar calibration procedure that is based on electronically generated radar targets. Experimental data suggest that precise absolute and differential calibrations can be achieved if data are obtained in an environment free from multipaths and if the generated targets are precisely located in the center of the radar's range gate. Experimental shortcomings associated with limited sampling resolution of the radar scan over the targets are also investigated.

Quantum materials exhibit exotic properties and intriguing collective phenomena arising from quantum degrees of freedom and strong many-body correlations, offering significant potential for novel technological applications. Photon-based spectroscopies, including angle-resolved photoemission spectroscopy (ARPES) and resonant inelastic X-ray spectroscopy (RIXS), have become essential tools for investigating these properties both in equilibrium and out-of-equilibrium.

This thesis develops advanced theoretical methods for photon-based spectroscopies. Chapter 2 introduces the Wannier-ARPES formalism, in order to calculate photoemission matrix elements from Wannier-function based slab tight-binding models. A key contribution is a microscopic theory for circular dichroism ARPES (CD-ARPES), enabling the mapping of wavefunction-related properties like orbital textures and band topology. Using Wannier-ARPES formalism, experimental CD-ARPES results can be accurately simulated, clearly distinguishing intra-atomic terms --which reflect local orbital angular momentum (OAM) -- and inter-atomic interference terms, which introduce universal photon-energy dependencies.

Chapter 3 explores dynamics observed through time-resolved ARPES (tr-ARPES), with particular focus on Floquet engineering. The chapter highlights the relation between Floquet states and multiphoton photoemission (mPP) with varying field strengths. In the intermediate field strength, photoemission probes Floquet quasienergy splitting. In a stronger field, complex non-adiabatic dynamics among Floquet states emerge, directly influencing observed mPP features. These insights are critical for realizing Floquet engineering and provide valuable views into nonlinear driven dynamics.

Chapter 4 focuses on resonant inelastic X-ray spectroscopy (RIXS) for strongly correlated systems, where various elementary excitations can be probed. A cluster model approach, combining exact diagonalization and an effective Anderson impurity model derived from \textit{ab-initio} parameters, is used to describe many-body excitations and the RIXS spectrum. The method successfully captures intensity variations of d-d excitations across magnetic phase transitions in YBaCuFeO5.

Overall, this thesis bridges theoretical advancements with photon-based spectroscopy experiments, providing tools not only to interpret complex spectroscopic data but also to guide future experimental studies for deeper insights into quantum materials.

This thesis presents the development and application of novel Neural Network Quantum States (NQS) for the simulation of strongly correlated quantum matter in continuous space. We augment traditional Slater-Jastrow-Backflow ansaetze with modern machine learning architectures, such as permutation-invariant DeepSets and permutation-equivariant graph neural networks (GNNs), dubbed MP-NQS, to construct compact, flexible and highly expressive variational models for both bosonic and fermionic systems.

A key innovation of this work is the incorporation of periodic boundary conditions into the network design of the NQS, allowing the accurate simulation of condensed matter systems and materials and the computation of thermodynamic properties from first principles. We demonstrate the effectiveness of this approach by simulating the phase diagrams of benchmark systems, including $^4$He in one and two dimensions and the homogeneous electron gas in three dimensions, capturing superfluidity, crystallization, and other emergent phenomena.

For fermionic systems, we further enhance the variational ansatz by integrating the Pfaffian determinant as anti-symmetric prior, allowing us to describe pairing correlations in ultracold Fermi gases across the BCSâ BEC crossover. In addition to bulk applications, we extend the framework to molecular systems, demonstrate its applicability to small molecules, and compute real-time dynamical properties using the time-dependent variational principle. Furthermore, we introduce an algorithm to access their finite-temperature properties and nuclear quantum effects through variational (path-integral) molecular dynamics.

Overall, the methods introduced in this work significantly broaden the applicability of variational quantum Monte Carlo by combining physical priors with the flexibility of deep learning. They pave the way for accurate, scalable, and transferable modeling of quantum matter, with potential impact on quantum chemistry, materials science, and strongly correlated condensed matter systems.

Hydrogels are widely used in cell biology and tissue engineering because of their high water content, biocompatibility, and adjustable mechanical properties. These qualities make them ideal for mimicking the extracellular matrix and creating soft devices that interact with biological tissues. However, their lack of electronic conductivity and low mechanical stiffness limit their application in bioelectronics and load-bearing uses such as bone tissue engineering. Efforts to overcome these limitations by adding conductive fillers or biominerals often reduce processability, especially through additive manufacturing techniques like direct laser writing (DLW) or extrusion-based 3D printing. To address these issues, I explore two strategies centered on the bottom-up, in-situ formation of functional fillers within hydrogels. By spatially localizing these fillers, the hydrogel gains electrical or mechanical functions not present in the original material, without losing compatibility with advanced manufacturing methods. In the first strategy, I use two-photon DLW to create high-resolution silver microstructures inside optically clear, soft hydrogel matrices. This technique reduces silver salts within the hydrogel through photoreduction, resulting in conductive features with resolutions as small as 5 µm and conductivities up to 1505 S/cm, without pre-mixing conductive fillers. This separates hydrogel formulation from filler addition and enables the creation of embedded or surface-exposed conductive pathways, opening new possibilities for soft, hydrogel-based bioelectronic devices. The second strategy uses a nature-inspired approach to 3D print biomineralized hydrogel scaffolds. Ureolytic bacteria are encapsulated in printable microgels to create a bioactive ink capable of inducing calcium carbonate mineralization in situ. This mineralization happens after printing, allowing independent optimization of the ink's rheological properties for printability. Spatial and temporal control over biomineralization results in scaffolds with mineral content up to 93% by weight. The microgels act as sacrificial templates, guiding the development of a 3D porous network that mimics trabecular bone architecture and achieves compressive strengths up to 3.5 MPa. This process uses only mild, biocompatible reagents and avoids high-temperature sintering. I demonstrate proof-of-concept applications in bone tissue engineering by printing complex porous structures and suggest potential use in art restoration. Together, these methods demonstrate that in-situ formation of functional fillers allows the spatial integration of conductivity and stiffness into hydrogels without compromising optical or rheological properties essential for additive manufacturing. I also outline future directions to enhance and expand these approaches, including using DLW to create 3D interconnects inspired by microfabrication techniques and developing hybrid scaffolds that combine electronic and mechanical functionalities for advanced tissue engineering. These innovations establish bottom-up hydrogel functionalization as a versatile platform for next-generation cell culture materials, bioelectronic devices, and engineered tissue scaffolds.

With the growing demand for miniaturized, self-powered devices, energy harvesting technologies that can exploit ambient and physiological energy sources have gained increasing attention. Beyond conventional batteries, strategies to convert energy from the human bodyâ such as heat or mechanical deformationâ into electricity are especially attractive for wearable electronics and distributed sensing platforms. These processes often rely on polar materials such as polyvinylidene fluoride (PVDF). However, PVDF, as a fluorinated polymer and so-called â forever chemical,â requires complex synthesis under high-temperature and high-pressure conditions. Achieving functional polarization also involves another high-temperature, high-voltage poling step. The semi-crystalline morphology of PVDF can be tailored through chemical modificationsâ most notably by copolymerizationâ to endow it with intrinsically adjustable electromechanical properties without the need for electrical poling. For example, one may evolve from simple P(VDF-co-trifluoroethylene) [P(VDF-TrFE)]â based copolymers to more complex terpolymers and even tetrapolymers. However, in practice this route has proven infeasible for large-scale manufacture owing to its synthetic complexity and the attendant high production costs. Moreover, fabricating PVDF into ultra-thin films is challenging: thick films are incompatible with microelectrode architectures, while thin films are prone to dielectric breakdown during poling.

To address these limitations, this thesis explores polymer brush architectures as an alternative platform for energy conversion. Through surface-initiated polymerization, polymer chains are grafted at one end and extend in an oriented "brush" conformation. This backbone alignment, in turn, compels the pendant polar moieties to adopt, more or less, the same orientation, thereby generating an intrinsic polarization in the as-grafted thin film without the need for any post-treatments such as electrical poling. This architecture provides intrinsic chain ordering, controllable thickness, and excellent conformality, making it highly compatible with micro- and nanoscale device integration.

This thesis demonstrates standard â textbook-qualityâ pyroelectric responses in polar-functionalized polymer brushes, and confirms that the observed behavior originates from fixed dipole moments in the chain architecture. The results further reveal that the pyroelectric performance primarily arises from the dense, brush-like regions of the film where the chains are highly stretched and aligned. In contrast, in thicker brushes, the upper segments tend to adopt disordered coil conformations that contribute negligibly to the overall polarization, effectively diluting the polarision density.

These findings establish polymer brushes as a tunable platform for pyroelectric energy conversion. Beyond simplifying processing requirements, they also offer a model system for probing structureâ polarization relationships and pave the way for flexible, conformal, and energy-autonomous interfaces in next-generation microelectronic systems.

This thesis explores how engineered bosonic environments in superconducting circuits can be harnessed to control light-matter interaction in regimes that extend beyond conventional cavity quantum electrodynamics (QED). We develop a scalable platform based on high kinetic inductance superconducting films, enabling the realization of compact, low-disorder coupled cavity arrays (CCAs) with fine spectral control. This platform is then used to study the dynamics of a giant atom in the superstrong coupling regime, where the coupling (G_n) of the qubit to mode (n) exceeds the free spectral range of the CCA, (\Delta\Omega_n).

In the first part of the thesis, we present the design and characterization of high-impedance CCAs using NbN thin films. This approach allows the implementation of resonators with footprints as small as (50 \times 75~\mu\mathrm{m}^2) and impedances of (Z_r \sim 1.5~\mathrm{k}\Omega), supporting free spectral ranges from hundreds of MHz down to 5~MHz. We engineer arrays with up to five resonators per unit cell and realize various band structures, including uniform, dimerized (Su-Schrieffer-Heeger SSH), and multigap configurations. We quantitatively characterize the CCA disorder using topological edge modes in SSH arrays, developing a likelihood-based method to extract resonator frequency fluctuations. Measurements on over 25 SSH devices confirm frequency disorder levels of (0.22^{+0.04}{-0.03}%), while maintaining low internal losses ((\kappa\textrm{int}/2\pi \sim 100~\mathrm{kHz})) and excellent scalability.

In the second part, we explore multimode QED in the superstrong coupling regime by embedding a transmon qubit into a two-band CCA. The qubit is non-locally coupled to seven lattice sites, forming a giant atom whose interaction with the bath strongly modifies the spectrum. Harnessing interference effects between the giant atom and the bath, we reach coupling ratios (G_n/\Delta\Omega_n > 10). Using a combination of spectroscopy and time-domain measurements, we extract the atomic participation ratio (APR) for all eigenmodes and observe a clear breakdown of the single-mode Jaynes-Cummings model, revealing mode-mode interactions mediated by the qubit.

Furthermore, we investigate directionality in the dressed CCA wavefunction induced by the qubit. We experimentally demonstrate asymmetric mode localization, leading to directional emission of single-photon states. By preparing the qubit in a bandgap-localized state and transferring it into the band, we show that emission can be routed predominantly to one side of the array. This result opens new opportunities for directional quantum optics in engineered superconducting systems.

Together, these results establish high-kinetic inductance CCAs as a powerful platform for analog quantum simulation of impurity models and structured light-matter interactions, offering new pathways to control dissipation, engineer photon-mediated interactions, and probe non-Markovian dynamics in circuit QED.