Pathogen proliferation has profound implications for its persistence, treatment strategies, and the induction and execution of protective immune responses. In vivo, pathogen proliferation rates are heterogenic, confronting the immune system with a variety of microbial physiological states. It is unknown if, and by what molecular mechanism, the immune response can distinguish these different states on a cellular level. Also, understanding the link between pathogen proliferation and immune cell dynamics could provide critical information on how infections can be controlled, and how to counteract pathogen persistence and antibiotic resistance. However, this question has never been addressed due to difficulties in studying the dynamics of immune cells and at the same time probing pathogen proliferation. In this project, we will make use of a novel in vivo reporter system that I have developed, in order to determine the role of the pathogen's proliferation for its interaction with the immune system. Specifically, we will (1) determine the tissue niche in which the pathogen proliferates, (2) investigate the differential dynamics of phagocyte-pathogen- and of T cell-APC-interactions related to pathogen proliferation rate, (3) manipulate the relationship between pathogen proliferation and immune cell dynamics by using proliferation-deficient mutants and optogenetic pathogen inactivation, (4) identify signaling pathways that are differentially induced in cells infected by high versus low proliferating pathogens, and test their involvement in differential immune cell dynamics related to pathogen proliferation. ImmProDynamics will for the first time provide insights into how cells of the immune system react to distinct pathogen proliferative states in vivo. This will greatly expand our knowledge of host-pathogen interactions, which will be critical for the design of efficient vaccines and antimicrobial therapy.

Physicians need to make many important decisions per day. One clinical example is the scheduling and dosage of chemotherapy treatments. A second example is the discrimination of atrial fibrillation from atypical atrial flutter, based on ECG data. Such important and complex decisions are usually based on expert knowledge, accumulated throughout the life of a physician and shaped by subjective (and sometimes unconscious) experience. It is not readily transferable and may be unavailable in rural areas. At the same time, the available imaging, laboratory, and basic clinical data is abundant and waits to be used. This data is not yet systematically integrated and often single data-points are used to make therapy decisions. More and more clinical decision making tasks will be modeled in terms of mathematical relations. I propose a systematic approach that supports and trains individual decision making. The developed ideas, mathematical models, and optimization algorithms will be generic and widely applicable in medicine and beyond, but also exploit specific structures, resulting in a patient- and circumstance-specific personalized medicine. This allows, e.g., a physician to first simulate the impact of his decisions on a computer and to consider optimized solutions. In the future, it will be the rare and unwanted exception that an important decision can not be backed up by consultation of a model-driven decision support system or based upon a systematic model-driven training. MODEST has a mathematical core. It builds on a comprehensive, interdisciplinary work program, based on disciplinary expertise in mixed-integer optimal control and existing collaborations with medical and educational experts. It is both timely, given the increasing availability of data and the maturity of mathematical methods, models, and software; as well as high-impact, due to the large number of clinical areas that may benefit from optimization-based decision support and training tools.

Computational models of vision often address problems that have a single and definite end-point, such as visual recognition: an example of this might be to find a ripe banana in a complex scene. However, not all computation is of this form. Visual information is processed continuously in sensory areas and the nervous system has the capacity to alter or halt an ongoing behavioral response to changes in incoming information. We can therefore react flexibly to updated sensory input or changed requirements for motor output. On the other hand, these same neuronal mechanisms must also support perceptual stability, so that noisy signals do not cause loss of a crucial goal. In project COGSTIM, I will investigate the functional neuronal networks that support the balance between perceptual flexibility and stability, within primate visual areas. I will use a highly innovative approach, combining dense electrophysiological recording with online (real-time) decoding of neuronal correlates of the subject’s perceptual choice, based on adaptive machine-learning algorithms. In order to control visual perception effectively and predictably, closed-loop electrical stimulation will be applied under dynamically adjusted feedback to identified neuronal circuits that causally modulate associated percepts. Crucially, this novel approach using joint decoding and stimulation in real time will allow me to target dynamically visual percepts, representing a significant advance in our understanding of on-going, continuous computations of the primate brain. Such developments offer promising bases for the future development of rehabilitative therapeutical protocols, as well as innovative brain machine interfaces suitable for real-world use.

A longstanding problem in combustion research is that there is no means to simultaneously measure the temperature and velocity in high-temperature, chemically-reacting flows, which is essential to probe complex turbulence-chemistry interactions found in advanced combustion systems. The aim of this project is to solve this problem using a novel laser-based temperature-velocity imaging technique developed by the host organisation (Lehrstuhl für Technische Thermodynamik (LTT), Otto-von-Guericke Universität Magdeburg, Germany), which uses thermographic phosphor particles as a flow ‘tracer’. The primary objective of the action is to increase the measureable temperature range via synthesis of new phosphor particles optimised for flow temperature sensing. LTT will collaborate with the Advanced Combustion and Propulsion Lab (ACP), Princeton University, USA, who have developed innovative synthesis methods capable of producing phosphor particles with specific physical and optical properties. At ACP, the candidate fellow (LTT) will learn how to produce phosphors using these advanced methods, and then return to LTT where the new materials will be characterised and proven in flames. A laboratory for phosphor particle production and luminescence characterisation will be installed at LTT. The candidate fellow will develop unique, interdisciplinary expertise in thermographic phosphors, materials that will be at the forefront of future remote sensing technologies. The project will result in completely new measurement capabilities for fundamental and applied research, allowing the design of cleaner, fuel-efficient engines in key automotive, aerospace and power generation industries, thereby using fewer resources and reducing environmental impact. These novel materials will find use in lighting and display technologies and biological sensing, maximising both the impact of the action and opportunities for future collaboration with ACP and other EU research institutions and industry.

Flows of two immiscible fluids separated by an interface, referred to as "two-phase flows", are ubiquitous in nature and central to many engineering applications. They contribute to the underlying principles and processes of a vast range of key sectors such as energy, transportation, manufacturing, and healthcare, and are relevant to the study of climate-change and disease-spreading. Most important to Europe's current challenges, the study of two-phase flows is instrumental in achieving the "European Green Deal" objective of reaching carbon-neutrality by 2050. Yet, despite their clear significance, our understanding of two-phase flows remains limited. Recently, computer simulations have become viable alternatives to experiments for their study, but they are still limited in terms of their flexibility, efficiency, and accuracy. The research programme proposed for this fellowship will provide a paradigm shift in the way two-phase flows can be simulated and therefore studied, with the development of the first high-order numerical framework for the accurate solution of their evolution in complex three-dimensional flow domains. This will entail increasing the order of representation of the local numerical approximations of the interface between the two phases, from linear to quadratic. This enhancement will in turn allow the development of high-order numerical schemes for the transport of this interface, and the estimation of the surface-tension force acting on it. These highly accurate schemes will be released to the research community in an open-source library. Not only will this enable the design of the next generation of low-emission energy-conversion technologies, which are crucially needed to reach Europe's environmental targets, but this will also yield substantial advances in manufacturing and health-related applications and in the prediction of climate-change, and will help devising future environmental and public health policies.