The Young International Academics postdoctoral programme (YIA) is a career development program proposed by the University of Luxembourg (Uni.lu) to nurture early-career postdoctoral applicants to gain momentum in interdisciplinary/intersectoral research. YIA applicants are international talents willing to propose and realize their own interdisciplinary research projects through a bottoms-up approach. YIA will welcome, in two calls, a total of 10 postdoctoral fellows with 36-month contracts over 5 years. YIA is open to all disciplines and sectors, involving Uni.lu in the drive towards an increased interdisciplinarity and intersectorality, which are strategic for Uni.lu, for the country and for Europe. A unique aspect of YIA is its integration into Uni.lu’s Institute for Advanced Studies-Luxembourg (IAS) created in 2019 to promote interdisciplinarity and outreach towards the society. This integration provides the YIA fellows with (1) a supervision for interdisciplinary and intersectoral research with more than 350 potential (co)-supervisors, and with about 350 potential public-private partners, (2) peer and cross-generational interactions/mentoring through the IAS fellows, invited distinguished senior scientists, early career researchers, and a mandatory academic or industrial secondment, and (3) a truly international flavour in an environment that cherishes diversity and excellence. YIA fellows recruited at Uni.lu are offered competitive contracts with mobility fellowships and access to excellent research infrastructures. A professor of Uni.lu provides supervision for the primary discipline of the applicant’s project and a co-supervisor covers the complementary discipline/sector. The YIA fellows are offered an “à la carte” training including mandatory courses, to suits the fellow’s career aspirations, increase the fellow’s interdisciplinarity and employability. The YIA programme will support a rapid ramp into a long-term Uni.lu funding of postdoctoral IAS fellows.

Computational design and discovery of molecules and materials relies on the exploration of increasingly growing chemical spaces. The discovery and formulation of new drugs, antivirals, antibiotics, catalysts, battery materials, and in general chemicals with tailored properties, require a fundamental paradigm shift to search in unchartered swaths of the vast chemical space. This is in stark contrast to current approaches, which start from (commercially available) libraries of compounds from various suppliers. Within the ERC Consolidator grant BeStMo (grant agreement ID 725291) we aimed to substantially advance our ability to model and understand the behaviour of molecules in complex environments. As a result, we successfully developed a set of machine learning and physics-based methods for covalent and non-covalent interactions that now allow an accurate and efficient modelling of molecules of increasing size (from 10 to 1000 atoms). These methods now enable routine calculations of quantum-mechanical properties of molecules throughout chemical compound space, provided that enough reference data is produced as a starting point for training. Within DISCOVERER, we aim to promote a paradigm shift in chemical discovery by inverting the selection pyramid by starting with pre-defined parameters from which new chemical entities are designed through machine learning and AI-enabled algorithms. We can do so by integrating these modules into a commercial platform: “Chemical Space Machine”. DISCOVERER’s main goal is to finalize the development of a commercial alpha version of “Chemical Space Machine” and setting up its commercialisation strategy.

Bias in the perception of bodily sensations (interoception) can be dangerous or even fatal; for example, if we do not recognize signs of a stroke or take medication anytime we feel the slightest change in our body. Research consistently reports that anxiety is closely related to interoception and (mis)interpretation of bodily sensations, but little is known about more fundamental processes underlying this relationship. The aim of this project is to test whether categorization as basic perceptual process links anxiety and interoception via generalization and perceptual decision strategies. Bundling the continuous flow of interoceptive information into distinct categories such as “pain” or “pleasure” can facilitate perception and coping. By assigning sensations to interoceptive categories (e.g., symptom groups) we can infer information about causes and consequences and apply coping schemata fast and efficiently. Research on visual perception, however, suggests that anxiety can be related to excessive generalization which may turn into harmful over-generalization, particularly if stimuli are misclassified. We will test whether also in interoception, anxiety is related to excessive category-related generalization. Furthermore, we will test whether individuals higher in anxiety are more prone to take a “better safe than sorry” strategy and misclassify benign sensations (e.g., heartbeat and breathlessness associated with exercise) more often as sign of disease. We will test our hypotheses of categorization linking anxiety and interoception in a newly developed research paradigm. In contrast to traditional paradigms, it allows distinguishing interoceptive accuracy and bias. The project is of theoretical relevance by targeting fundamental processes linking anxiety and interoception. Furthermore, it is of clinical relevance by testing the relationship between anxiety and interoceptive classification strategies, which are related to choices in health behaviour.

Shape-shifting systems would offer unique innovation capabilities which have inspired fiction and driven exciting science and engineering research over the past decades. Such adaptable, multi-functional, shape-shifting creatures that could freely assume any desired form, mimic natural or engineered systems and even perform tasks beyond the capabilities of any human or conventional machine are indeed exciting prospects which could change the way we see world around us and interact with it. Yet shape-shifting systems, which include soft and reconfigurable robots, remain in their infancy. We are still to estimate physical limits for such technology and thus remain unable to predict which of all the exciting potential applications are actually achievable. In this project, through a continuous interaction between Robotics, Soft-Matter Physics, Computer Science and Computational Mechanics, with my host, I will devise novel theories and algorithms, implemented in open-source software which will enable to effectively control shape-shifting systems. Such predictive and control tools remain the major obstacle to designing and constructing actual shape shifters with unique and adaptable properties. The impact of shape-shifting devices would span the whole of engineering and medicine. Such systems could be used in communication and entertainment to physically reproduce virtual moving and interacting entities. In medicine, they could be injected into the blood stream, adapt to physiological conditions, enter the most remote, sinuous and confined areas in organs, treat them or invest them with required enhancements, e.g. cochlear implants for earing aids. These exciting opportunities, however, require the ability to effectively control these shape-shifting and adaptable systems, which is the aim of this Fellowship.