This Nordita Program is devoted to theoretical and observational studies of the interstellar medium of galaxies across cosmic time, and to their implications in shaping future line-intensity mapping experiments which have recently generated a tremendous interest in the Community of astrophysicists and cosmologists. The program is particularly timely because the advent of new facilities, such as ALMA full array, JWST (launch spring 2019), and E-ELT (2024), will provide a wealth of high resolution multi-wavelength spectroscopic data on the ISM of galaxies across cosmic time. Moreover, the program will bring together experts from different areas as we aim gathering astrophysicists, working on galactic and extragalactic observation, theoreticians devising simulations, astrochemists, and cosmologists interested in the large scale structure of the Universe. The program has been conceived with a bottom-up structure that, from ~pc scales, relevant for star formation, will zoom-out up to ~Mpc scales relevant for intensity mapping experiments.

This school, intended for PhD students and junior researchers in quantum phenomena and condensed matter physics, will consist of short courses on topics from Short courses from Quantum Matter, Quantum Information and Quantum Sensing, from theory to computations and experimental results.

In recent years there have been exciting new developments at the interface between elliptic integrable systems, special functions and quantum field theory. The aim of this workshop is to obtain a better understanding of the emerging links between these topics and to help bring out further unexpected connections in the future. This will be achieved by bringing together researchers from diverse areas in mathematics and physics, for a week of lectures and informal discussions. The workshop is the continuation of a series (Kyoto 2004, Bonn 2008, Leiden 2013, Vienna 2017). It is a satellite meeting of String Math 2019, which takes place on Uppsala 1-5 July. The main themes of the meeting are: Elliptic integrable systems, Elliptic hypergeometric functions, Elliptic and classical Painlevé equations, and New special functions emerging from quantum field theory

A week of workshops at the frontiers of quantum physics. Hosted by Frank Wilczek in collaboration with Stockholm University, Nordita and Wilczek Quantum Center at Shanghai Jiao Tong University.

Growing amount of molecular biological data combined with current advances in modeling of complex systems provide unprecedented opportunities to understand biological evolution in a quantitative way. A quantitative description of an evolving system is the first step towards prediction and control, and it opens new exciting directions for highly interdisciplinary research. The central questions are: (i) to what degree we can predict the outcome of biological evolution, (ii) what features of the system are predictable and (iii) which features confer predictive value for a quantitative description of the system. This program brings together theoretical and experimental physicists, experimental biologists with an interest in quantitative modelling and mathematicians with interest in biological systems. We aim to create a dialog between researchers of different fields and to inspire future collaborations. In addition, further developments in this field would have significant translational impacts, e.g., by optimizing vaccines against evolving viruses, designing strategies for personalized cancer therapy and by providing insights to the problem of antibiotic resistance.

The conference will cover cutting-edge non-perturbative methods in quantum field theory, as well as mathematical aspects of integrability and its more traditional applications in condensed-matter physics and statistical mechanics. Solvable models play a valuable a role in theoretical physics, as they illustrate general concepts in a simpler setting and provide insights into the qualitative features of more complex phenomena.

In the last few years there has been renewed interest in QCD and hadronic dynamics using holographic gauge/gravity duality, resurge of the large N methods, progress in string models, integrability, unitarity and bootstrap and more. Also, many new experimental results were reported by ALICE at LHC regarding collectivization in pp and pA collisions, by LHCb at CERN and the B- and C-factories regarding the existence of exotics and the spectroscopy of heavy-light systems, and more recently the reporting of 2 neutron star mergers by the LIGO collaboration and its constraint on the nuclear dense equation of state. The conference will bring together practitioners of these interdisciplinary fields to discuss these exciting new developments, and explore the relevance of the holographic framework for addressing these observations.

Recent advances in the band theory of crystalline materials have singled out topology as a key ingredient in the modern classification of matter, with major impact on measurable electronic properties. Topological band theory has also grown into an emerging paradigm in many areas of physics, and is now used to characterize metamaterials, including photonic, atomic, acoustic, and elastic systems, in both the quantum and classical regimes. As our understanding of topology in physics widens, the incorporation of out-of-equilibrium phenomena is gaining in importance.

Gravitational waves promise a new window into the highest-energy events in the evolution of the universe. The recent LIGO/Virgo detections of gravitational waves from the mergers of binary black holes and binary neutron stars and have ignited interest in the future direction of gravitational wave astronomy. A space-based laser interferometer, pioneered by NASA's LISA concept and the European Space Agency's eLISA program and ESA's recent spectacularly successful LISA Pathfinder mission, would enable direct detection of gravitational waves in the milliHertz range. A lower frequency range would allow detection of supermassive black hole mergers, tracing the galaxy merger history and serving as cosmic sirens to probe the universe's expansion history, as well as precursors for the LIGO sources. A space-based detector would also be sensitive to stochastic gravitational wave backgrounds produced by unknown physics operating in the very early universe, including an electroweak phase transition. This Nordita program will bring scientists together to engage in an effort to characterize and detect sources contributing to the gravitational wave background from the early universe, and the implications for new physics at the TeV scale and beyond.

Machine learning has entered the field of quantum matter with applications covering quantum materials and the many-body problem. For example, interpretable and computationally-efficient machine learning models are able to capture the structure-property relationship in materials science. In case of the many-body problem, machine learning architectures provide versatile wavefunctions that lead to accurate results and prove to be more flexible than traditional methods. Conversely, methods in physics have also influenced the development of machine learning methods in the case of tensor networks. The workshop will feature talks by leading experts combined with the talks of younger participants to present a broad picture of the activities and best ideas on the use of ML methods in quantum matter.

In the search for dark matter (DM), one particular focus is on light and ultra-light dark matter, i.e. sub-GeV mass dark matter from a hidden dark sector with a new force interacting with the standard model or ultra-light DM with mass range from 10−22 eV to keV. The arguably most popular example of the latter class is the axion, invoked to solve the apparent absence of CP violation in Quantum Chromo Dynamics. Detection of these particles poses new challenges to potential sensor materials: very small energy depositions, magnetic properties and anisotropic response to particle interactions for example become crucial requirements. The challenge of finding suitable materials fits well with recent developments in solid state physics: Motivated by the exponential growth of computational power and the resulting data, we witness the rapid adoption of functional materials prediction within the framework of materials informatics. Here, methods adapted from computer science based on data-mining and machine learning are applied to identify materials with requested target properties.

The question of how particles and droplets can grow in a turbulent environment is of great current interest in many fields, in astrophysics, cloud microphysics, in biology, and in the engineering sciences. For example, coagulation and condensation in turbulent clouds turn microscopic cloud droplets into rain drops. In astrophysics, planetesimals are thought to form by aggregation of microscopic dust grains in the turbulent environment surrounding a forming star. In both cases, turbulence is believed to be a crucial factor for particle growth. Yet the microscopic mechanisms determining this growth are far from understood. In the past few years there has been substantial progress in understanding the mechanisms that determine how particles move in turbulence, albeit mostly for simplified model systems. The challenge is now to understand how these mechanisms lead to rapid particle growth.