The implementation of CRISPR/Cas technology has already revolutionised biology and biotechnology. However, for plant breeding its full potential has hardly been applied. The gene pool of a plant species carries a tremendous amount of information regarding how to survive best under various biotic and abiotic stresses. Although countless wild varieties of crops have been safeguarded in gene banks worldwide, much of their genetic information cannot be used in agriculture. Often, adverse and/or favourite traits are linked due to the fact that respective genes are located within close proximity, on the same chromosome. Breeding aims not only to break linkages between such traits but also to tightly fix favourable linkages. In cereals, half of the genome cannot be accessed by classical breeding. The aim of this proposal is to develop techniques based on CRISPR/Cas technology, to engineer plant breeding on the molecular level. With the use of the Cas9 nuclease of S. pyogenes and multiple sgRNAs, it became possible to induce several genomic changes at the same time. The aim of this proposal is to perform genome engineering on a multidimensional level by not only inducing multiple DNA lesions (single and double stranded breaks) but also by applying different Cas9 orthologues to simultaneously target DNA recombination factors directly to the sites of action, or indirectly by influencing their expression. Thus, site-specific initiation of recombination should be coupled with pathway choice, resulting in novel approaches for breaking or fixing linkages. Techniques for genome restructuring, like inversions and translocations, should be established as well as efficient induction of somatic and meiotic crossovers. Therefore, the basis should be laid for combining the best available traits of a species, resulting in transgene free crop plants for a sustainable agriculture. Furthermore, the Cas9-controlled transfer of chromosomal segments between species will also be addressed.

The new frontier for weather prediction is the so-called subseasonal time scale of two weeks to two months ahead. To take preventive measures at an early stage, reliable forecasts on this time scale are becoming increasingly important for multiple socio-economic sectors. Subseasonal predictability can be gained from recurring patterns in the Earth system. ASPIRE will focus on one of these, namely modes of tropical convective variability. Long-standing systematic errors due to the parametrization of processes in numerical weather prediction models prevent the predictability of these modes from being exploited. Simply running models at a resolution high enough to resolve tropical convection is not feasible due to high computational costs. Taking advantage of three recent developments, ASPIRE will explore new ways to better exploit the intrinsic predictability of tropical convective modes without exhausting the currently available computing resources. The uniqueness of ASPIRE is its cross-disciplinary approach that builds on my experience in atmospheric dynamics and predictability, numerical modeling, and machine learning (ML). First, ASPIRE will identify the source regions and pathways of tropical forecast errors that prevent the intrinsic predictability from being exploited using a new set of subseasonal ensemble hindcasts. Second, ASPIRE will quantify for the first time the added value of locally confined kilometer-scale resolution in the source regions identified before, and generate probabilistic predictions from deterministic forecasts through ML-based post-processing. Third, to enable simulations at kilometer-scale resolution in operations, ASPIRE will develop ML approaches that emulate the integrated effect of the resolved convection in the tropics at substantially reduced costs. If successful, this approach would be a breakthrough towards improved operational weather forecasts at substantially lower computational costs, for a global socio-economic benefit.

The project proposal MASSIF aims at improving the fundamental understanding and control of interfaces of a battery type based on Li active materials all solid state batteries (ASSLB). The main objective is the investigation and optimization of the interfaces developing between the solid electrolyte and the electrochemically active material particles electrodes. The acquired knowledge would allow the design of optimized interfacial layers (also called artificial Solid Electrolyte Interfaces, art-SEI) capable of warrant stable interfaces. The art-SEI should allow intimate contact between the active material and the conductive particles. Our research program involves hybrid electrolytes in combination with solid state electrolytes (SSE); this approach proves to be useful to generate an art-SEI. We are going to pay attention to the positive electrode-solid electrolyte interface, which has been studied as much as the metal Li solid electrolyte interface. With this in mind, they are going to use Li2S as a positive electrode, and the last task of the project will develop a full cell using Si as a negative electrode. Si was selected as a negative electrode to avoid the problems related to Li; it is known that Li reacts with most of the SSE. The project also takes in to account the training of the researcher and the career development plan to achieve the goals and independence of the researcher. The interdisciplinary and network of the host institution will be also used to push the career of the researcher forward.

LiVoX will make our NMR microchip sensor market-ready for novel applications such as foodstuff testing, compound screening for example on cell-based assays, or biopsy monitoring for hospitals. By turning NMR sensors into consumables, LiVoX will achieve a paradigm shift that will open up these new markets. We are convinced that the sensors will be readily accepted by the market, since they already work with established and installed NMR hardware, and provide dramatically increased NMR measurement sensitivity. LiVoX will take a mass-producible NMR microchip sensor that is currently at technology readiness level (TRL) 6 at a university laboratory, and scale up the manufacturing towards wafer-scale mass production, by outsourcing the majority of the manufacturing steps. LiVoX will implement quality control procedures for production and sensor function, and will perform extensive beta testing at application sites, to drive the microchip sensors right up to commercialisation readiness at TRL 9. It is the clear goal of LiVoX to transfer the microchip sensor manufacturing process to a startup company at the end of this one year ramp-up period. LiVoX will remove the last barriers that remain for introduction of the microchip sensors into the marketplace.