Understanding complex networked systems is key to solving some of the most vexing problems confronting humankind, from discovering how dynamic brain connections give rise to thoughts and behaviors, to detecting and preventing the spread of misinformation or unhealthy behaviors across a population. Graduate training, however, typically occurs in one of two dimensions: experimental and observational methods in a specific area such as biology and sociology, or in general methodologies such as machine learning and data science.

With more and more students seeking to gain sufficient expertise in mathematical and computational methods on top of domain-specific laboratory and social analysis methodologies, a greater demand for more efficient training is emerging. This National Science Foundation Research Traineeship (NRT) award to Indiana University will address this growing need with an integrated dual PhD program that trains students to be “bidisciplinary” in Complex Networks and Systems (CNS) and another discipline of their choosing from the natural and social sciences. It will seamlessly integrate traditional education with interdisciplinary hands-on research in a culture of academic and human diversity.

 

Applications Due December 1

Source: cns-nrt.indiana.edu

The Intellectual Theme Initiative “Just data” at Central European University is looking for an exceptional candidate to fill one open postdoctoral position in data science, broadly defined.

The research will be conducted with the research group directed by Prof. Roberta Sinatra and Prof. Michael Szell, with unique opportunities for collaborations with other excellent faculty at Central European University. The group currently focuses on quantitative projects at the boundary of computational social science and network science, including science of science, social dynamics, urban sustainability, data visualization, and fundamental questions in complex systems.

We welcome applicants with strong background in fields including statistical physics, applied mathematics, machine learning, artificial intelligence, social network analysis, complex systems, or other closely related fields. Our priority is to attract technically strong researchers who are interested in asking bold, new questions with data. A stated passion in data analysis is a must; excellent organizational and interpersonal skills are essential. The applicant will conduct research using large data sets, and be involved in the development of interactive data visualization tools.

Source: www.robertasinatra.com

Richard H. Thaler has incorporated psychologically realistic assumptions into analyses of economic decision-making. By exploring the consequences of limited rationality, social preferences, and lack of self-control, he has shown how these human traits systematically affect individual decisions as well as market outcomes.

Limited rationality: Thaler developed the theory of mental accounting, explaining how people simplify financial decision-making by creating separate accounts in their minds, focusing on the narrow impact of each individual decision rather than its overall effect. He also showed how aversion to losses can explain why people value the same item more highly when they own it than when they don’t, a phenomenon called the endowment effect. Thaler was one of the founders of the field of behavioural finance, which studies how cognitive limitations influence financial markets.

Social preferences: Thaler’s theoretical and experimental research on fairness has been influential. He showed how consumers’ fairness concerns may stop firms from raising prices in periods of high demand, but not in times of rising costs. Thaler and his colleagues devised the dictator game, an experimental tool that has been used in numerous studies to measure attitudes to fairness in different groups of people around the world.

Lack of self-control: Thaler has also shed new light on the old observation that New Year’s resolutions can be hard to keep. He showed how to analyse self-control problems using a planner-doer model, which is similar to the frameworks psychologists and neuroscientists now use to describe the internal tension between long-term planning and short-term doing. Succumbing to shortterm temptation is an important reason why our plans to save for old age, or make healthier lifestyle choices, often fail. In his applied work, Thaler demonstrated how nudging – a term he coined – may help people exercise better self-control when saving for a pension, as well in other contexts.

In total, Richard Thaler’s contributions have built a bridge between the economic and psychological analyses of individual decision-making. His empirical findings and theoretical insights have been instrumental in creating the new and rapidly expanding field of behavioural economics, which has had a profound impact on many areas of economic research and policy.

Source: www.nobelprize.org

We may soon have detailed images of life’s complex machineries in atomic resolution. The Nobel Prize in Chemistry 2017 is awarded to Jacques Dubochet, Joachim Frank and Richard Henderson for the development of cryo-electron microscopy, which both simplifies and improves the imaging of biomolecules. This method has moved biochemistry into a new era.

A picture is a key to understanding. Scientific breakthroughs often build upon the successful visualisation of objects invisible to the human eye. However, biochemical maps have long been filled with blank spaces because the available technology has had difficulty generating images of much of life’s molecular machinery. Cryo-electron microscopy changes all of this. Researchers can now freeze biomolecules mid-movement and visualise processes they have never previously seen, which is decisive for both the basic understanding of life’s chemistry and for the development of pharmaceuticals.

Electron microscopes were long believed to only be suitable for imaging dead matter, because the powerful electron beam destroys biological material. But in 1990, Richard Henderson succeeded in using an electron microscope to generate a three-dimensional image of a protein at atomic resolution. This breakthrough proved the technology’s potential.

Joachim Frank made the technology generally applicable. Between 1975 and 1986 he developed an image processing method in which the electron microscope’s fuzzy twodimensional images are analysed and merged to reveal a sharp three-dimensional structure.

Jacques Dubochet added water to electron microscopy. Liquid water evaporates in the electron microscope’s vacuum, which makes the biomolecules collapse. In the early 1980s, Dubochet succeeded in vitrifying water – he cooled water so rapidly that it solidified in its liquid form around a biological sample, allowing the biomolecules to retain their natural shape even in a vacuum.

Following these discoveries, the electron microscope’s every nut and bolt have been optimised. The desired atomic resolution was reached in 2013, and researchers can now routinely produce three-dimensional structures of biomolecules. In the past few years, scientific literature has been filled with images of everything from proteins that cause antibiotic resistance, to the surface of the Zika virus. Biochemistry is now facing an explosive development and is all set for an exciting future.

Source: www.nobelprize.org

On 14 September 2015, the universe’s gravitational waves were observed for the very first time. The waves, which were predicted by Albert Einstein a hundred years ago, came from a collision between two black holes. It took 1.3 billion years for the waves to arrive at the LIGO detector in the USA.

The signal was extremely weak when it reached Earth, but is already promising a revolution in astrophysics. Gravitational waves are an entirely new way of observing the most violent events in space and testing the limits of our knowledge.

LIGO, the Laser Interferometer Gravitational-Wave Observatory, is a collaborative project with over one thousand researchers from more than twenty countries. Together, they have realised a vision that is almost fifty years old. The 2017 Nobel Laureates have, with their enthusiasm and determination, each been invaluable to the success of LIGO. Pioneers Rainer Weiss and Kip S. Thorne, together with Barry C. Barish, the scientist and leader who brought the project to completion, ensured that four decades of effort led to gravitational waves finally being observed.

In the mid-1970s, Rainer Weiss had already analysed possible sources of background noise that would disturb measurements, and had also designed a detector, a laser-based interferometer, which would overcome this noise. Early on, both Kip Thorne and Rainer Weiss were firmly convinced that gravitational waves could be detected and bring about a revolution in our knowledge of the universe.

Gravitational waves spread at the speed of light, filling the universe, as Albert Einstein described in his general theory of relativity. They are always created when a mass accelerates, like when an ice-skater pirouettes or a pair of black holes rotate around each other. Einstein was convinced it would never be possible to measure them. The LIGO project’s achievement was using a pair of gigantic laser interferometers to measure a change thousands of times smaller than an atomic nucleus, as the gravitational wave passed the Earth.

So far all sorts of electromagnetic radiation and particles, such as cosmic rays or neutrinos, have been used to explore the universe. However, gravitational waves are direct testimony to disruptions in spacetime itself. This is something completely new and different, opening up unseen worlds. A wealth of discoveries awaits those who succeed in capturing the waves and interpreting their message.

Source: www.nobelprize.org