Shelf-to-basin sedimentary processes entail intricate interactions between the ocean sub-mesoscale and the near-seafloor sediments. These processes hold significant multidisciplinary importance, given their ecological impact, relevance to marine geo-hazards, and role in shaping continental margins and global climate. However, there's a gap in quantitative tools to model and characterize these processes simultaneously considering oceanographic and near-seafloor sediments properties. In GEM-SBP I will use ultra-high-resolution seismic reflection data (UHRS) to create a set of modelling techniques able to couple both domains. The Southeastern Levant Basin (SLB) will serve as a natural laboratory for studying the interaction between the sedimentological and oceanographic domains. This semi-enclosed basin features a single sediment source and a multitude of morphologically complex features formed by sediment transport mechanisms. I will first develop novel seismic processing workflows tailored for ocean and near-seafloor sediments imaging that will consistently yield a seismic image depicting both domains and their interaction. I will then develop and implement an iterative geostatistical seismic inversion procedure able to invert the processed UHRS for high-resolution ocean (i.e., temperature and salinity) and near-seafloor geotechnical models. I intend to leverage the most recent advances in deep learning to build a spatial regression model and predict these properties in 3-D by integrating spaceborne earth observation data. Our models will provide insights into the sediment transport mechanisms dynamics and will pave the way for a new set of methodologies for studying these phenomena on a global scale. I intend to raise society's awareness about the global impact of oceanographic and seafloor processes, through outreach, communication and dissemination activities aiming to foster a multi-stakeholder environment (i.e., industry, academia and public) within the project.

Electronic products are progressively smaller and more powerful, resulting to an exponential increase in the generated residual heat. Their effective and environmental-friendly cooling is therefore, of upmost importance for many applications such as Data Centres, Fuel Cells, Insulated-Gate Bipolar Transistors, Lithium-Ion Batteries and Photovoltaic Cells, with a market value of several billions of dollars worldwide. Flow Boiling within micro-passages has been proven as one of the most efficient cooling strategies for such High-Power Density Electronics. However, such solutions, are not yet commercially available. This is due to a lack of a deep understanding of the underpinned flow and transport processes and unresolved ambiguities in micro-scales and hence, of reliable and easy-to-use thermal design tools for small-scale components. REFINE aims to give light at such crucial ambiguities, utilising a synergic combination of novel Volume Of Fluid (VOF) based numerical simulations and tailored high-resolution experimental diagnostics. Dr Andredaki will develop a novel cutting-edge simulation tool starting from an already enhanced VOF solver that she has been developing in the last years. This will be validated against parallel advanced experimental measurements on flow boiling, that she will perform using single and multiple parallel micro-channel heat sinks. The final optimised and validated numerical solver will then be applied for a wide series of parametric simulations that in combination with additional laboratory measurements will form a unique database that will lead to the development of novel, physics-based design correlations for flow boiling micro-channel heat sinks. The word-leading expertise of Prof. Moreira, who will supervise this project, on the experimental techniques for microscale boiling, guarantees the highest level of knowledge transfer, enabling Dr Andredaki to further develop her skills and enhance her future career opportunities in academia.

The central challenge in energy systems is to balance cost-efficiency, environmental responsibility, and living standards. Multi-objective optimization in such systems is critical for striking compromises among stakeholders and ensuring competitiveness and sustainability. As energy systems face uncertainty, volatility, and environmental regulations, holistic optimization becomes more critical. However, three main challenges currently hinder energy system optimization: complex models demanding substantial computational power, a lack of holistic and robust approaches, and the use of real-time data for proper operation and analysis. Although each of these challenges has been addressed individually, no contribution was able to bring them all together in a consistent methodological framework. This is the main objective of this fellowship. This fellowship will be conducted at IST/University of Lisbon under the supervision of Prof. Henrique Matos, and aims to bridge these gaps by integrating machine learning, systems optimization, uncertainty analysis, and real-time operation. It seeks to develop advanced surrogate generation techniques, emphasize system integration and holistic analysis, optimization under uncertainty, and the use of real-time data for operation. These methods will be applied to real industrial challenges in an industrial secondment. This research project will develop methods and tools to address such challenges and contribute to a sustainable, profitable, and responsible European economy.

The relevance of RealIMP lies in the need to develop and optimize sustainable technologies to remove contaminants of emerging concern, such as microplastics and organic micropollutants, from water, contributing to the access to cheap and clean water sources to large, urbanized centers and small rural communities. Solar light-driven photocatalysis has great potential to overcome this challenge due to the virtually zero cost of solar power, and the low cost and abundance of the well-established TiO2 photocatalyst. However, it remains limited to niche applications due to its low photonic efficiency and elevated costs. RealIMP will tackle these issues by using doped TiO2, tuned for the visible light of the solar spectrum, and pilot scale photoreactors adapted and optimized to real-life applications, using statistical Design of Experiments methods. Another innovation will be the development of ultrasonic acoustic sensors to detect the microplastics’ spatial distribution in both static and continuous-flow conditions and to assess their adsorption on the catalyst’s surface via geophysical inversion. The biodegradability of the microplastics after photocatalytic experiments will be assessed under simulated real conditions using a respirometer. To further simulate real conditions, the influence of the presence of organic micropollutants, such as antibiotics, including their adsorption on microplastics, will be investigated regarding microplastic's degradability. Empirical models of prediction for photocatalysis and biodegradability kinetic rates, energy consumption, and economic costs will be obtained. Besides providing cutting-edge technological and scientific results in this field, as well as interdisciplinary training to broaden my expertise, RealIMP will prioritize outreach activities to reduce the gap between academy, industry and the general public such as workshops, educational, and raising awareness campaigns.

Humans have fascinating skills for grasping and manipulation of objects, even in complex, dynamic environments, and execute coordinated movements of the head, eyes, arms, and hands, in order to accomplish everyday tasks. When working on a shared space, during dyadic interaction tasks, humans engage in non-verbal communication, by understanding and anticipating the actions of working partners, and coupling their actions in a meaningful way. The key to this mind-boggling performance is two-fold: (i) a capacity to adapt and plan the motion according to unexpected events in the environment, (ii) and the use of a common motor repertoire and action model, to understand and anticipate the actions and intentions of others as if they were our own. Despite decades of progress, robots are still far from the level of performance that would enable them to work with humans in routine activities. ACTICIPATE addresses the challenge of designing robots that can share workspaces and co-work with humans. We rely on human experiments to learn a model/controller that allows a humanoid to generate and adapt its upper body motion, in dynamic environments, during reaching and manipulation tasks, and to understand, predict and anticipate the actions of a human co-worker, as needed in manufacturing, assistive and service robotics, and domestic applications. These application scenarios call for three main capabilities that will be tackled in ACTICIPATE: a motion generation mechanism (primitives), with a built-in capacity for instant reaction to changes in dynamic environments; a framework to combine primitives and execute coordinated movements of head, eyes, arm and hand, in a way similar (thus predictable) to human movements, and model the action/movement coupling between co-workers in dyadic interaction tasks; and the ability to understand and anticipate human actions, based on a common motor system/model that is also used to synthesize the robot’s goal-directed actions in a natural way.