Coralline algae are an important group of benthic marine organisms that form unique, but endangered, biotopes on the seafloor. Coralline algae, have been a notable component of the Mediterranean realm for millions of years and persist to this very day. This is in spite of the multiple environmental perturbations that the Mediterranean had experienced, such as its decupling from other oceanic basins in the past to anthropogenic forcing in the present. As such, the deposits from by coralline algae can allow us to study some of the most significant environmental perturbations in the Mediterranean, explore habitat resilience and improve our understanding of this environment in general. To do so, this project will explore multiple occurrences of coralline algae in the south central Mediterranean using a one of a kind comprehensive data and sample set. This includes both extensive seafloor and sub-seafloor acoustic data, over 1.5km of cored material penetrating multiple ancient coralline rich units, ROV dives from modern living coralline biotopes and compilation of data from the rest of the region. The project will implement cross-disciplinary integration of this data set in a holistic fashion, inducting the researcher into seafloor studies. Together, these elements will give the fellow the ability to explore novel and inventive tools for detailed investigation of these deposits. The combined approach will produce a comprehensive model of the behaviour and evolution of corallines in the Mediterranean over key periods of the last 25 Myr. This work will expend the fellows’ multidisciplinary capacity and will promote the career development of this young researcher within the European community. The products of this project will be made available to the public across multiple venues to be used to promote truism and provide bases for planers and dissection makers in respect to the possible effect on coralline algae rich environments.

Foreign direct investment (FDI) flows are conducted by INDIVIDUAL investors (abbreviated as IFDI), which have been strongly promoted by Immigrant Investor Programs (IIPs). Twenty-one out of twenty-seven of the member states of the European Union (EU) establish IIPs to attract wealthy investors in order to receive investment capital; change the economic structure and social status of undeveloped regions, make a name for the country on the international market, and as an economic diplomacy strategy. While the FDI motivations of enterprises have been extensively studied; the motives of IFDI remain undeveloped. This limitation is certainly a missing puzzle piece in the scientific world, causing a lack of a theoretical basis to explain a real phenomenon. Simultaneously, demand-side motive ambiguity might impact the design of IIPs, leading to their inefficiency. Governments also lack a base to take a consistent stance on IFDI. This proposal aspires to provide a scientific and practical basis to fully understand the variation of IFDI, thereby aligning policy with its evolution. The overall aim is to develop the patterns of IFDI, its nature, and trends. Qualitative research methods with a variety of tools, mainly based on naturally occurring data associated with the opinions of the participants - knowledge co-producers. Two theoretical frameworks are developed: IMS12 to identify the drivers and nature of IFDI and VIRA to forecast IFDI. The results of this project, in addition to fulfilling its own objectives, will also serve as the foundation for further research in several scientific fields such as economics, sociology, and politics. This project is expected to run for 24 months and is hosted by the University of Malta in Malta, including a 4-month secondment at the University of Graz, Austria.

Some of the biggest open problems in modern cosmology are the nature of the cosmic dark sector, the discrepancy between the theoretically predicted versus the observed value of the cosmological constant, and the growing cosmological discordances and tensions between different observational probes. Notably, the Hubble constant, which describes how fast the Universe is expanding when measured locally, has an enormous statistical disagreement with that inferred from the early Cosmic Microwave Background data. These inconsistencies, in turn, necessitate the formulation of new physics beyond the standard cosmological model. Current and ongoing observations, together with upcoming surveys, will produce large volumes of data, whose accumulation and processing will require an upgradation and increase in the sophistication of our statistical tools before applying them to specific problems. Thus, we propose to build a deep learning architecture using advanced statistics in machine learning algorithms like neural networks to be integrated into cosmological community codes for emulated parameter inference. This will help us to select, in a model-independent way, generic features of some cosmological theories that satisfy all observations. Utilising the power of deep learning will be an ideal space to investigate new physics in the observational sector and discriminate between models that are degenerate in terms of current observational approaches, fostering the development of data-driven science as a valuable companion to the model-driven paradigm. The fellowship will contribute to the researcher's career development by acquiring advanced skills in machine learning approaches using Bayesian statistics and developing skills within the cosmological community through a series of events designed to disseminate his results to the broader public. The project will also serve to consolidate and extend the researcher's network of professional contacts within Europe and beyond.

BioGeMT will merge the strengths of the University of Malta (UM) in ICT and Genomics by hosting an expert Bioinformatician and Team within the Centre for Molecular Medicine and Biobanking, that will forge collaborations with existing highly specialised molecular genetics research groups. The goal is for UM to sustainably develop the field of Bioinformatics and the knowledge base and Machine Learning skills required to translate patient derived multi-Omic and clinical data into a better understanding of disease. The advancements made in high throughput -Omic technologies are locally hampered by a lack of bioinformaticians. There are benefits to rapidly developing bioinformatics in Malta: The population's genetic background and availability of large families is advantageous in elucidating the genetic basis of disease. The UM has expertise in biobanking, genetics, genomics and epidemiological research, enabled through strong ties with Mater Dei Hospital. BioGeMT complements and builds on previous investments (ERDF, TWINNING, local R&I funds) used to train researchers, build specialised labs and set up highly phenotyped disease-oriented sample collections with multi-Omic data, one with >2000 whole genomes and RNA-Seq data sets. Bioinformaticians would unlock the full potential of ongoing studies helping to overcome the problematic lack of genetic data from populations other than those of Northern European origin. BioGeMT would boost the international attractiveness, innovation and ability of the UM to compete successfully for international research funding, including IMI, Horizon Europe, charity foundations and industrial grants, by creating a highly specialised Bioinformatics centre in Southern Europe. In the long term this will increase the attractiveness of Malta to bioinformatics companies in line with the national smart specialisation strategy to expand into ICT for health, counteract brain drain, generate jobs and enable collaboration with untapped non-EU regions.

Silicon solar cells are the most commercialized photovoltaic devices due to their high-power conversion efficiencies (PCE). Over the different types of silicon substrates for solar cells, monocrystalline silicon is the one with the highest PCE reported. Monocrystalline silicon is commonly grown by the Czochralski method, a process in which a small seed crystal is dipped into a melt in a crucible, pulling the seed upwards to obtain a single crystal. Nonetheless, by the same process, two types of intrinsic defects can be incorporated: additional atoms (interstitials) or missing atoms (vacancies); additionally, the crucible used is generally silica, so the result is an oxygen contaminated ingot. Oxygen tends to react with vacancies, seriously affecting the PCE of the synthesized solar cells. Nitrogen has long been known to simultaneously suppress interstitial and vacancy related defects, the higher the nitrogen concentration, the lower the defect size, which is highly favourable for defect annealing; besides, strongly enhances oxygen precipitation. Unfortunately, quantitative data on the chemical and physical properties of nitrogen in silicon are rare, so the mechanism through which it reacts with intrinsic defects and oxygen is still relatively unknown. In consequence, it is not possible to know what variables should be modified to improve the quality of the crystal. The main idea of this project is to investigate the effect mechanism of nitrogen on grown-in oxygen precipitates. A complete understanding would lead us to find the ideal conditions to dope silicon with nitrogen, in order to reduce defect sizes and the oxygen amount to a minimum, so as to reach the maximum PCE in a monocrystalline silicon solar cell.