Spintronic devices perform information storage and processing based on the spin degree of freedom. Materials with complex magnetic order, such as ferrimagnets, antiferromagnets and chiral magnets are promising candidates for next-generation spintronic devices with ultrafast speed, enhanced robustness and unique functionalities. However, several fundamental obstacles prevent their efficient control with established approaches based on magnetic fields and electrical currents. MAWiCS will overcome these obstacles by introducing the magneto-acoustic control of magnetization in these complex spin systems. The advantage of MAWiCS’ approach is based on the following hypotheses: Microwave frequency phonons can excite and control antiferromagnetic spin waves and magnetic skyrmions lattices with high efficiency. The uniaxial magnetic anisotropy induced by magneto-acoustic interactions can be used for full modulation of antiferromagnetic resonance frequencies. Magneto-acoustic waves can propagate in topologically protected skyrmion lattice edge-states with reduced magnetic damping. MAWiCS will develop innovative experimental approaches to take advantage of symmetry, topology and exchange-enhancement effects for highly efficient control of spin dynamics in complex spin systems. Consequently, MAWiCS’ results will allow for the first time to: 1) Generate nanoscale spin waves from acoustic pulses in ferrimagnets and antiferromagnets. 2) Control skyrmions by acoustic lattices and realize nanoscale topological acoustics 3) Excite and detect antiferromagnetic spin waves by acoustic two-tone modulation MAWiCS’ results will pave the way for the technological realization of magneto-acoustic spintronic devices, enable antiferromagnetic magnonics and realize topological magnon transport. Ultimately, MAWiCS will thus pioneer a new class of information technology concepts that do not only offer increased performance but also novel functionalities.

The year 2022 was a year of catastrophic extreme climatic events at an unprecedented scale. Rivers around the globe drying up, China's extreme heat wave, extreme floods in Pakistan and in the United States, are clear examples of a climate change scenario that is expected to be more and more pervasive in the next years. Despite evidence of the increase in frequency and intensity of extreme events, the effects of the extreme oscillations between droughts and floods on streams carbon cycling are still uncertain. Alpine streams are important contributors of inland water carbon and changes that occur in alpine streams can profoundly alter downstream reaches affecting the global carbon budget and in turn exacerbate climate impact in the future. PeriCarb, EFA aims at understanding how periphyton biofilms community composition changes due to extreme flow change events and the reduction in the recovery time for the microbial community between events. It will link biofilm changes in community composition to changes in carbon cycling and in greenhouse emissions. By linking microbial ecology and carbon biogeochemistry changes in alpine streams PeriCarb will inform on the impact of climate change on global carbon budgets. PeriCarb will constitute a multidisciplinary project joining the applicant's previous experience with the excellence of the host institution and a group of established collaborators. It will provide advances for the field of carbon biogeochemistry and allow the applicant to return to Europe to establish as a researcher.

Techniques for separating fluid mixtures are important in many industries like the chemical and pharmaceutical industry. The most relevant of these separation techniques, like distillation and absorption, are based on mass transfer over fluid interfaces. Results from molecular thermodynamics, which have recently become available, show that for many industrially important mixtures a strong enrichment of components occurs at the fluid interface. There is a striking congruence between shortcomings of the present design methods for fluid separations and the occurrence of that enrichment. It is the central hypothesis of the present research that the enrichment leads to a mass transfer resistance of the fluid interface which has to be accounted for in fluid separation process design. The fact that it is presently neglected causes unnecessary empiricism and inconsistencies in the design. ENRICO will advance the knowledge on the enrichment of components at fluid interfaces using a novel combination of two independent theoretical methods, namely molecular simulations with force fields on one side and density gradient theory coupled with equations of state on the other. This will enable reliable predictions of the occurrence of the enrichment and its magnitude. These results will be combined with the theory of irreversible thermodynamics to establish for the first time a model for the mass transfer resistance of the interface due to the enrichment. On that basis, a new approach for designing fluid separation processes will be developed in ENRICO, which will lead to more efficient and robust designs. The theoretical results will be validated by experiments from laboratory to pilot plant scale, and the benefits of the new approach will be demonstrated. ENRICO will thus establish a link between molecular physics and engineering practice. The results from ENRICO will have a major impact on chemical engineering world-wide and change the way fluid separation processes are designed.

Neuromorphic computing uses networks of artificial neurons highly interconnected by artificial synapses to perform vast data processing tasks with unmatched efficiency, as needed, for instance, for pattern recognition or autonomous driving tasks. The synaptic connections play a paramount role to create better hardware realizations of these networks. However, it is very complex to realize large interconnectivity by electronic circuitry. COSPIN overcomes this connectivity constraint by using the eigen-excitations of the magnetic system - the spin waves - to connect state-of-the-art artificial neurons based on spintronic auto-oscillators. COSPIN’S main goal is to create and experimentally validate innovative physical building blocks for a novel nano-scaled, all-spintronic network structure which incorporates all necessary properties for neuromorphic computing including high nonlinearity, interconnectivity and reprogrammability. By design, COSPIN works at the boundary between oscillator-based computing and wave-based computing. It uses interference, frequency-multiplexing, and time-modulation techniques as well as spin-wave amplification to significantly increase the connectivity between neurons. Reprogramming of the network is implemented by a direct physical link to magnetic memory solutions as well as by reconfiguring spin-wave circuits. By using coherent wave interference and nonlinear wave interaction, COSPIN paves the way for novel coupling phenomena for complex artificial neural networks far beyond the state-of-the-art of current hardware realizations. Using cutting-edge micromagnetic simulations enhanced by inverse design methods, the artificial networks will be designed and tested prior to their nano-fabrication. Experimental investigations will be mainly carried out using micro-focus Brillouin light scattering. This allows for local investigation of the individual neurons and synapses, and significantly simplifies the interpretation of the network dynamics.

Research will be conducted on character theory of finite groups, specifically on characters' values and fields of values. Two new ambitious problems will be investigated. One is a global-local problem, concerning irreducible characters of a group and of his Sylow normalizers, and it will be investigated using machine learning methods. The other consists in searching for a family of irreducible characters which "behaves well" in relation with elements of prime power order. My research will have a considerable impact in the field of character theory and it will have possible applications in other fields of group theory. The use of of machine learning methods in my research may have an impact extending beyond group theory, as it could suggest a similar use in other fields of mathematics. Research to be conducted in Kaiserslautern, under the supervision of prof. Malle, eminent researcher in the field of character theory of finite groups of Lie type. Training on machine learning to be conducted in the CS department of the hosting university. Further training will be conducted on character theory of non-abelian simple groups, and possibly as a teaching assistant in group theory.