“Pro-Membrane” is an extensive project which has been structured to face multiple tasks of different levels of innovation and risk. In its longest-term and most ambitious goal, Pro-Membrane, aims to face the still not solved challenge of stabilize and crystallize Membrane-Proteins (MPs). This study will use, as innovative tool, well-ordered and disordered functionalizable amphiphilic peptoids for structural determinations. Peptoids could indeed succeed where other classes of molecules previously failed, answering to the pressing need of a new method able to establish the specific interactions to stabilize and crystallize each MP. Their peculiar nature helps them to easily cross the membrane and their modular synthesis give access to an infinite combination of molecules. Despite their interest, a systematic and comparative study of their physical properties and self-assembly capacity was never done before. The multidisciplinarity of this work, between Organic Chemistry, Biophysics, and Structural Biology will be achieved through a strict collaboration between all the participants. Training through research and implementation activities have been planned to maximize the candidate’s profile in order to support his career perspectives.

Contemporary research endeavours aim at equipping artificial systems with human-like cognitive skills, in an attempt to promote their intelligence beyond repetitive task accomplishment. However, despite the crucial role that the sense of time has in human cognition, both in perception and action, the capacity of artificial agents to experience the flow of time remains largely unexplored. The inability of existing systems to perceive time constrains their potential understanding of the inherent temporal characteristics of the dynamic world, which in turn acts as an obstacle to their symbiosis with humans. Time perception is without doubt, not an optional extra, but a necessity for the development of truly autonomous, cognitive machines. TIMESTORM aims at bridging this fundamental gap by shifting the focus of human-machine confluence to the temporal, short- and long-term aspects of symbiotic interaction. The integrative pursuit of research and technological developments in time perception will contribute significantly to ongoing efforts in deciphering the relevant brain circuitry and will also give rise to innovative implementations of artifacts with profoundly enhanced cognitive capacities. Equipping artificial agents with temporal cognition establishes a new framework for the investigation and integration of knowing, doing, and being in artificial systems. The proposed research will study the principles of time processing in the human brain and their replication in-silico, adopting a multidisciplinary research approach that involves developmental studies, brain imaging, computational modelling and embodied experiments. By investigating artificial temporal cognition, TIMESTORM inaugurates a novel research field in cognitive systems with the potential to contribute to the advent of next generation intelligent systems, significantly promoting the seamless integration of artificial agents in human societies.

With the 2012 discovery of the Higgs boson at the Large Hadron Collider, LHC, the Standard Model of particle physics has been completed, emerging as a most successful description of matter at the smallest distance scales. But as is always the case, the observation of this particle has also heralded the dawn of a new era in the field: particle physics is now turning to the mysteries posed by the presence of dark matter in the universe, as well as the very existence of the Higgs. The upcoming run of the LHC at 13 TeV will probe possible answers to both issues, providing detailed measurements of the properties of the Higgs and extending significantly the sensitivity to new phenomena. Since the LHC is the only accelerator currently exploring the energy frontier, it is imperative that the analyses of the collected data use the most powerful possible techniques. In recent years several analyses have utilized multi-variate analysis techniques, obtaining higher sensitivity; yet there is ample room for further improvement. With our programme we will import and specialize the most powerful advanced statistical learning techniques to data analyses at the LHC, with the objective of maximizing the chance of new physics discoveries. We aim at creating a network of European institutions to foster the development and exploitation of Advanced Multi-Variate Analysis (AMVA) for New Physics searches. The network will offer extensive training in both physics and advanced analysis techniques to graduate students, focusing on providing them with the know-how and the experience to boost their career prospects in and outside academia. The network will develop ties with non-academic partners for the creation of interdisciplinary software tools, allowing a successful knowledge transfer in both directions. The network will study innovative techniques and identify their suitability to problems encountered in searches for new physics at the LHC and detailed studies of the Higgs boson sector.