Luxembourg National Research Fund

The mass digitization of historical sources and the exponential growth online of born-digital sources has catapulted the discipline of history from an “age of scarcity” to an “age of abundance”. Making sense of such “big data of the past” requires new approaches to data management, mining, visualization, and interpretation – an endeavour that poses multiple challenges to the disciplines of both history and data science. We propose to address this problem with a new Doctoral Training Unit (DTU): “Deep Data Science of Digital History”. Our DTU’s key objective is to train PhD students and engage with them in a critical study of historical data by bringing together intellectual and technical resources generated across disciplines, particularly from digital history, social sciences and data science. To achieve this objective, we propose to deepen the interdisciplinary collaboration between digital history and computer science by exploring the concepts of deep history and deep data science.

Based on the theoretical framework of “digital hermeneutics”, this DTU will tackle critical questions in historical data science by focusing on three thematic & methodological pillars:
1) deep data & knowledge; 2) deep analytics & learning; 3) deep visualization & interpretation.

• Deep data & knowledge addresses the challenges of creating digital datasets which, in the field of history, are generally characterized by their heterogeneity of data and their unstable or fluid nature in terms of volume and integrity. The axis will focus on analysis of characteristics, formats, histories, and infrastructures of historical data and train our PhD students in historical data criticism and traceable data management.

• Deep analytics & learning engages with state-of-the-art approaches in machine learning technologies and the use of artificial intelligence for analyzing large historical datasets. The aim of interdisciplinary training is to evaluate the heuristic potential of statistical modelling techniques, sensitivity analysis, and simulations for developing historical questions and interpretations and to confront computational methods with the rigor of quantitative methods at scale in historical sciences.

• Deep visualization & interpretation enters epistemological discussions about how visualization techniques and dynamic interfaces transform historical imagination and interpretation. Based on recent trends in explainable AI, information visualization, and human-computer interaction, the aim of this axis is to promote critical debates about how historical arguments can be turned into “graphic arguments”, and how new techniques of representing big historical datasets can be turned into explorative modes for the temporal and spatial sampling of historical information.

Spanning these three thematic pillars, two transversal structures will frame the Doctoral Training Unit as a coherent and shared interdisciplinary endeavour: the first builds on the conceptual work realized within the previous DTU “Digital History and Hermeneutics”, serving as a theoretical “Überbau” for organizing the collaborative work within the thematic units; the second transversal structure “Deep time & history” will serve as a common ground for discussing fundamental questions of how the collection, analysis, visualization, and interpretation of the big data of the past affects our understanding of time.

We designed the thematic pillars and transversal structures to function as “trading zones” – a concept originating from the sociology of knowledge describing the affordances and risks involved when trying to build fruitful bridges between disciplinary traditions and communities of practice. Inspired by a critical reflection of the metaphor of “deep time”, our DTU will problematize complex notions of multi-layered temporalities both in a “horizontal” (longue-durée) and “vertical” (superimposed temporal regimes).

The need to manage resources sustainably has never been clearer. Fiber reinforced polymers (“composites”) combine low density and excellent structural performance, making them highly attractive in this context. From lightweight vehicles to wind turbine blades to corrosion-resistant bridges, composites are already having an impact on greenhouse gas (GHG) emissions. To realize their promise, however, a truly sustainable composite lifecycle is essential, and three critical needs must be addressed:

1. Composites must be made from bio-based and / or recovered materials.

2. These sustainable components must be effectively combined, and the resultant composites must be efficiently formed into robust structures designed for reprocessing and repair.

3. At end-of-life, these composites must be readily disassembled and their components recovered and reused.

These needs are translated into three research axes with 9 PhD topics distributed among them. This effort will extend the state-of-the-art in disciplines from computer simulations and polymer chemistry to composite (re-)processing, recycling and design for repair. A skilled team from the Luxembourg Institute of Science & Technology (LIST) and the University of Luxembourg (UL) will build on existing and anticipated collaborations, leveraging strong expertise in all relevant fields to guide a critical mass of PhD candidates in the execution of highly impactful scientific research.

Students will benefit from the collaborative, multidisciplinary nature of the work, a strong training plan, high quality facilities and infrastructure, a stimulating international environment, and experienced supervisors with the demonstrated ability to coordinate and execute research at this scale. The work aligns with institutional and national research priorities, and its success will jump-start the careers of the student participants, enable the broader, more sustainable use of composites, generate new economic activity and benefit society.

The last 150 years of research in immunology have led to our current understanding of the human immune system and our ability to manipulate its function therapeutically. Immunology has always been at the crossroads of biomedical research, providing both crucial information on basic biological processes and on clinical application. The future of immunology also lies in the integration of several interdisciplinary research fields. This will help to understand the immune system as a central component of the body that links different physiological networks to maintain cellular homeostasis.

The education and training of future researchers must take place at the interface of traditionally separate disciplines such as mathematics, computer science, biology and epidemiology, with the aim of linking them together. This will provide the basis for a transition to personalized medicine and is important to enable healthy living as well as healthy aging. This development will be supported by the use of largescale data sets that allow diseases to be classified by their molecular nature rather than symptom-based. High-throughput technologies can generate a large amount of immune-associated data on a genome wide scale. It is therefore essential to use these data in system based approach to analyze, predict and evaluate the function of the immune system.

This research program focuses on two aspects of immune regulation that have the potential to broaden our global understanding of the body’s immune response. Immunometabolism and systems immunology approaches can be applied to different models and disease states, leading to a truly holistic understanding of immunology on the cellular, organ and organismal level. Systems biology approaches have the potential to make a significant contribution to the understanding of disease mechanisms based on an iterative process of quantitative data collection, computational modeling, and experimental validation of the proposed hypotheses. Our understanding of these processes has increasingly benefited from the significant advancement of multiple high-throughput technologies (“Omics”) in the recent years. However, because of this development, immunological research is facing new challenges.

In the “”omics”” era of life sciences, we are faced with steady increasing amounts of data to process. The combination of information science and hypothesis-driven research is the most promising approach that will culminate in the development of innovative solutions for personalized medicine.

Building on our first NEXTIMMUNE PRIDE program, we propose to continue with this multidisciplinary training program for PhD students in immunology research in Luxembourg. This proposal takes advantage of the network and expertise generated in NextImmune, which was built on the path leading from big data to personalized medicine, and focusses on open questions in immunology. Through our multidisciplinary education program, we aim to train the next generation of scientists to meet the challenges of an ever-changing scientific world.

The MATHCODA DTU is a doctoral training program covering a coherent set of themes around the ideation and study of novel mathematical tools for dealing with high-dimensional and complex data structures, with applications ranging from life sciences to engineering and finance. The main rationale behind our project is the necessity of training a new generation of researchers, possessing a full mastery of the mathematical tools that are needed for modelling and statistically analyzing data structures displaying non-standard features, such as high-dimensional components, complicated topological structures or long-range dependence. The proposed DTU will be led by seven supervisors from Luxembourg University – each of them a scientific leader, specializing in one or more facets of mathematical statistics, probability theory and their applications. According to our vision, the interaction between the distinct research groups participating in MATHCODA will provide to the doctoral candidates an invaluably rich learning environment, as well as an ideal springboard for their subsequent careers.

More specifically, the doctoral program will be organized around the following themes:

– Stochastic analysis and statistical inference for stochastic systems with dependent noise

– Functional limit theorems for random graph statistics and changepoint analysis

– Statistical inference for diffusion models and high-dimensional particle systems

– Econometrics and asset pricing

– Universal estimation strategies

– Directional Statistics and applications to bioinformatics

– Random fields modeling and inference

The HYMAT Doctoral Training Unit (DTU) based at the Luxembourg institute of Science and Technology (LIST), more specifically in the Materials Research & Technology (MRT) department, will educate and train the next generation of doctoral candidates in research on the development and characterisation of advanced functional materials and material systems, with a specific focus on catalytic materials for hydrogen generation and utilisation. The research activities will address key materials challenges faced in establishing specific technologies that will underpin the Hydrogen Economy and as such the HYMAT DTU offers to the doctoral candidates the opportunity to perform research on a topic at the forefront of societal and scientific discussion.

A pool of 8 doctoral candidates will be supervised by recognised experienced scientist and be granted access to state-of-the-art infrastructure. The doctoral candidates will be trained to become experts in their field, to develop innovative skills and to understand the broader challenges of the energy transition and Hydrogen Technologies, specifically from a materials R&D perspective. The Doctoral Training Unit is structured such that training will emphasise the important role of functional materials as a key component in the sustainable economies needed to achieve emissions reduction targets. Training will range from broader science and materials science education, to transdisciplinary and transferable skills and importantly the socio-economic / ecological importance of the Hydrogen Economy.

The HYMAT project will be built upon and consolidate existing R&D strengths and infrastructure at LIST, reinforcing collaboration in materials research in the MRT department. The R&D objective of the HYMAT DTU is to enhance the fundamental understanding and performance of advanced functional materials focusing on hydrogen production and utilisation, specifically for water splitting and fuel cell (FC) devices. The medium to long term research objective is to go beyond the state-of-the-art and deliver performance profiles in line with international performance targets, namely high performance catalytic materials to promote both the rate limiting Oxygen Evolution Reaction (OER) (relevant to water splitting) and the Oxygen Reduction Reaction (ORR) (relevant to FCs), as addressed from an experimental and theoretical standpoint.

Strategically, the HYMAT DTU fits well with the research strategy of the host institution. The MRT department at LIST has identified materials R&D on the production of “Clean H2” and its use in “Platinum Group Metal-free Fuel Cells” as core priority areas. THE HYMAT DTU will provide the framework and the resources to streamline, integrate and interlink the ongoing efforts on Hydrogen Materials at MRT and consolidate the interdisciplinary approach suitable in hydrogen technologies. At the national and supranational levels, the positioning of the HYMAT DTU is further substantiated by the increasing focus (e.g. through the Green New Deal) on delivering a H2-based economy for the EU.

Building on our successful first Doctoral Training Unit in Cancer Biology (CANBIO) within the first FNR PRIDE program, we are keen to continue to provide state-of-the-art training for PhD students in cancer research in Luxembourg. This proposal takes advantage of the network and expertise generated in CANBIO1, while incorporating novel scientific insight and addressing pertinent questions in the cancer field.

In CANBIO1 our research focused on tumor escape mechanisms, investigating key aspects of intrinsic and induced escape mechanisms in response to therapy. The present application aims to generate new insight into the tumor ecosystem (TE) and to harness this knowledge for improved treatment options. Over the years, cancer research has moved from a tumor-centric view to an organ-type view of cancer recognizing the importance of the tumor microenvironment (TME) in tumor growth and progression. The clinical relevance of the TME is highlighted by the observation that pre-existing immune parameters correlate with patient survival, exemplified by the emergence of the immunoscore for the classification of gastrointestinal cancer. However beyond the immediate TME which includes stromal cells, infiltrating immune cells, extracellular matrix, available nutrients and secreted factors, cancer development and metastatic growth are also under distant organismal influences such as systemic immunity, the gut microbiome and metabolic conditions.

Newly described forms of communication e.g. through extracellular vesicles have been recognized as important components of cellular interactions in the local TME and systemic circulation. In the context of immunotherapy there is growing appreciation that immune cell manipulation and recruitment need to be considered beyond the tumor site. For example, glioblastoma (GBM), a deadly brain cancer, is not only characterized by a highly immunosuppressive TME, but also displays systemic immune suppression, including immune cell dysfunction and T cell lymphopenia.

Furthermore it is recognized that cancer metabolism is not limited to metabolic reprogramming of cancer cells, and increasing efforts aim to understand the metabolic interactions between cancer and non-neoplastic cells. Dietary interventions are studied to regulate nutrient availability at an organismal level and insight into the impact of the microbiome on cancer progression and treatment is gaining momentum.

Such processes are of utmost importance in the context of metastasis, where colonization far away from the primary tumor relies on immunosuppressive and tumor supportive factors at a systemic level. Understanding the nature of these complex dialogues is at the center of this research program, which will allow for improved therapeutics that simultaneously target the cancer cell and multiple components of the TE.