The production of sustainable energy that generates a clear and net greenhouse gas saving is one of the main EU objectives. Nature provides with the most efficient energy infrastructures known today. A deeper understanding of photosynthetic systems, and how energy transfers within its different subunits would show us the way to efficient energy flow, opening the path to the fabrication of highly efficient solar cells and transmission networks. The aim of the COMPLEX project is to provide insight to the energy conversion and transfer in complex molecular systems. We will develop and identify methods used in quantum mechanics, statistics and quantum information theory to model the energy transport in complex networks. Optimisation methods from mathematics and engineering will be applied to analyse biolological and artificially fabricated systems. Findings will be transfered to other related systems such as nano-engineered networks of nanofibers and polymers, which are designed for efficient transport with applications in organic solar cells and light-emitting devices and to further complex systems. This breakthrough in the state-of-the-art in terms of understanding energy flow in complex networks will take place by applying and transferring the specialised knowledge of Dr. Mirta Rodriguez, a Physics Researcher back from a research career break and specialist in the field of complex quantum systems dynamics, through scientific leadership to the research team at ZIB, where the computational infrastructure and knowledge and will be available to the Fellow. Specialist knowledge in applied technologies and innovation management gained by the Felow during her career break from R&D in Physics, will ensure that the results attained within the COMPLEX project will not only remain as basic research. Providing the European energy sector proper understanding of the energy flow mechanisms in complex networks will be revolutionary for sustainable energy production and energy

Managing and conserving forest ecosystems in Europe and worldwide is an indispensable component of climate adaptation and climate change mitigation strategies. Precise and up-to-date information about the health and the carbon balance of forests are, hence, critical to assess the current state of forests, trigger appropriate countermeasures against forest loss, and develop improved management strategies. Advances in both Earth observation and artificial intelligence have paved the way for the automation of forest monitoring using satellite time series data, including optical, radar, and LiDAR measurements. The forest maps produced by today's approaches, however, are still often limited to coarse resolutions and/or to relatively small spatial areas. To overcome those limitations, the AI4Forest project brings together experts in artificial intelligence, applied mathematics, computer science, spatial remote sensing, and climate change. AI4Forest strives for both conceptually novel AI methods for forest monitoring as well as for scalable AI methods that allow to process large amounts of data efficiently and at low cost. The resulting techniques will facilitate the generation of detailed forest maps at a very high spatial and temporal resolution for the whole European continent and the entire world, including tree species identification, mortality and biomass carbon stocks changes down to the level of individual trees.

Computational devices and closely-related information-processing technologies are among the most revolutionary inventions of the past century. Another groundbreaking discovery of the 20th century was quantum mechanics. Developed as a theoretical model to describe physics at the atomic level, it fundamentally changed our understanding of the world around us. The field of quantum computation stems from both of them. Its main objective is to understand how quantum mechanics changes our understanding of computation, especially the division between feasible and infeasible problems. Recent developments in quantum algorithms indicates that various optimization problems can be solved much faster on a quantum computer. Optimization problems permeate our society, they are key for the efficient operation of industry, logistics, and for countless other tasks that are crucial to the functioning of our modern society. Also, optimization problems are notoriously hard, and solving many of them precisely is out of reach of the most powerful modern computers even for modestly-sized instances. The radical vision of this proposal is to push the use of quantum computers for optimization tasks much further, developing new quantum algorithms which go well beyond the capability of even the best classical computers we have today. We aim at both general-purpose algorithms that can be used for a large variety of applications as well as more application-focused algorithms. We consider both continuous and discrete optimization, quantum algorithms for mixed-integer programs, as well as applications for machine learning, logistics, big data and physics. Our approach includes recent and exciting developments like quantum dynamic programming and graph sparsification. We are also interested in studying the QAOA-type algorithms, which can be executed even on really small quantum computers like the ones available today.

Clinical diagnosis is seldom definitive. Clinical data are noisy and sparse, and often support multiple diagnoses and potential therapies. To decide how best to treat a patient requires identifying the many possible outcomes for an individual and their corresponding probabilities. In this project we will apply the mathematics of uncertainty quantification, developed for automotive, geological and meteorological predictions, combined with biophysical models of individual patient physiology and pathophysiology to predict patient outcomes and their corresponding probabilities. This will demonstrate how patient specific computational models can be used to make prospective predictions to guide procedures and inform uncertain clinical decisions. The use of uncertainty quantification and predictive patient specific models will be applied to patients with atrial fibrillation. Atrial fibrillation (AF) is the most common cardiac arrhythmia in the UK. In patients who do not respond to drug treatment, the pathological regions of the atria are removed or isolated through catheter ablation. However, up to 40% of patients with advanced (persistent) AF require further ablations to treat atrial tachycardia (pathological but regular activation) that develops after they have had an initial ablation to treat their AF. To reduce the number of additional procedures, this project will predict the probability that a patient will develop atrial tachycardia and the path that the atrial tachycardia will take, based on measurements recorded at the time of the initial persistent AF ablation procedure. If successful this approach would guide preventative ablations during the initial procedure to reduce the need for repeat procedures.