Ultracold atomic gases have emerged as ideal quantum simulators, as they enable experimentalists to study the interplay between the properties of a quantum many-body system and the interactions between its constituents with unmatched accuracy. However, in spite of impressive progresses, an atomic quantum simulator of highly correlated fermionic matter, able to address both phenomena of exotic superfluidity and itinerant ferromagnetism, still awaits experimental demonstration. Such a system needs to be built from the ground-up by carefully harnessing the underlying few-body physics. In CriLiN I will develop and test a new kind of Atomic Quantum Simulator of unequal-mass spin-1/2 fermions with long-range, multi-body resonant interactions. In order to do so, I will exploit the still unexplored 6Li-53Cr Fermi-Fermi mixture which, thanks to its special mass ratio of M/m=8.8, exhibits unique few-body properties that strongly favour the many-body phases of our interest. Indeed, on the “molecular side” of an interspecies s-wave Feshbach resonance, the Cr-Li system supports a real (virtual) stable universal trimer (tetramer) state, while benefiting from quantum-interference induced suppression of three-body recombination processes. At the few-body level, this will allow for the first time to resonantly tune multi-body, long-range p-wave interactions. At the many-body level, this will allow both to investigate Stoner's model of itinerant ferromagnetism and to greatly enhance the possibility to attain elusive superfluid regimes or topologically non-trivial p-wave superfluids. In CriLiN I will: (i) realize a degenerate 6Li-53Cr Fermi-Fermi mixture and identify intra- and inter-species Feshbach resonances suitable for our simulator; (ii) unveil and characterize stable cluster states and exploit them to resonantly tune three- and four-body elastic interactions; (iii) demonstrate the suppression of inelastic pairing processes in repulsively interacting mixtures.

Humans have an impressive ability to form action plans in several domains of cognition; for example, planning routes to goals in spatial navigation, or the necessary steps to assemble complex objects, alone or together with other persons. However, the computations that underlie human individual and social planning remain largely unknown. This proposal aims to explain the ways humans face three key forms of uncertainty arising in planning domains; namely, uncertainty about task structure, action sequences, and the contributions of self and others to cooperative plans. To this aim, it advances a radically new theory about human planning, within a Bayesian approach that has been successfully adopted to explain uncertainties arising in perception and control. The theory under scrutiny is that humans plan using probabilistic inference based on hierarchical predictive codes (HPCs): compressed information or task abstractions that afford a powerful form of uncertainty-minimization, by highlighting salient junction points of the problem at hand, analogous to saliency maps for visual search. The methodology will combine empirical and computational modeling methods, to systematically validate the hypotheses of HPC theory about human planning in the face of uncertainties. A cornerstone of the methodology consists in conducting model-based analyses of human participants' behavior while they solve navigation-and-building tasks, alone or in dyads. This approach will permit us to compare the predictions stemming from HPC with those of alternative planning theories and ultimately, to understand the computations that underlie human planning. This ambitious proposal will produce groundbreaking advancements in our understanding of a high-level executive function - planning - while also contextualizing it within the influential theory of predictive processing. Our results will have important implications for psychology, neuroscience, philosophy, AI and robotics.

Prostate cancer (PC) is the fifth leading cause of cancer-related death worldwide. PC often presents in its Multidrug resistant form leaving the patient few survival chances. New approaches are required to overcome resistance-related problems in PC and nanomedicine holds a lot of premises to effectively contribute in this battle. In this frame POLAR STAR aims at the implementation of combination therapy to treat castrate-resistant PC. Our strategy is to exploit innovative nanotechnology to administer contemporarily different therapeutic agents that synergically exert their activity across multiple oncogenic pathways. We plan to use new mixed polymers based on biocompatible cyclodextrins as these building blocks are already FDA approved. Simple organic chemistry is pursued to implement target selectivity and in vivo tracking of the polymeric nanocarrier. The design is guided by the final goal, the future clinical application of nanocarriers to improve PC treatment, keeping in mind upgrade of the nanocarrier systems to large scale production. We will focus on the latest PC drugs suffering from side effects and emerging resistance as multiple cargo to be loaded. Full chemico-physical characterization of the systems is planned as well as assessment of the efficacy of loaded nanocarriers in cell cultures with different drug responsivity profiles. In order to reduce animal experiments POLAR STAR will take full advantage of 3D prostate tissue models for biological tests, an apporach at the forefront in drug design. We plan to reach our goal bringing together the expertise of the fellow and the supervisor supported by the private and academic teams hosting the fellow during secondments. POLAR STAR creates a multidisciplinary environment where all actors, public and private, will benefit from reciprocal transfer of knowledge. During the project the fellow will have the possibility to become a complete researcher improving also complementary skills.