The goal of this course is to help produce a community of leaders that is equally knowledgeable in neuroscience, cognitive science, and computer science and will lead the scientific understanding of intelligence and the development of true biologically inspired AI.

Course/Program Dates:
Aug 03, 2025 - Aug 24, 2025
Application due date:
Mar 24, 2025

The basis of intelligence – how the brain produces intelligent behavior and how to endow machines with human-like intelligence – is arguably the greatest problem in science and technology. To solve it, we will need to understand how natural intelligence emerges from computations in neural circuits, with rigor sufficient to reproduce similar intelligent behavior in machines. Success in this endeavor will ultimately enable us to understand ourselves better, to produce smarter machines, and perhaps even to make ourselves smarter. Today’s AI technologies are impressive but quite different from human intelligence. We still do not understand the mechanisms underlying the robustness, the generalization, and the continual learning capabilities of biological intelligence. The synergistic combination of cognitive science, neurobiology, engineering, mathematics, and computer science holds the promise of significant progress. Elucidating how human intelligence works will in turn lead to more sophisticated AI algorithms. The goal of this course is to help produce a community of leaders that is equally knowledgeable in neuroscience, cognitive science, and computer science and will lead the scientific understanding of intelligence and the development of true biologically inspired AI.

Apply at: www.mbl.edu

Applications for the third Complexity Global School (CGS) are now open. Like last year, the school will be hosted at Universidad de los Andes (Uniandes), in Bogotá, Colombia, but for the first time, applicants from all countries are eligible to apply. Roughly 60 students will be selected for the school, which will run July 28 – August 8, 2025. Supported by the Omidyar Network and the Ford Foundation, the school is free for all admitted students — tuition, room, board, and a travel stipend are included. Applications are due by March 2, 2025.

Apply at: www.santafe.edu

To celebrate 20 years since the launch of Journal of the Royal Society Interface, we are launching a Perspective competition. The winner will receive a prize of £1,000.  

More at: royalsociety.org

The Max Planck Institute for Demographic Research (MPIDR) is recruiting highly qualified Post-Docs or Research Scientists to join the Department of Digital and Computational Demography, headed by MPIDR Director Emilio Zagheni. The positions will be filled in the Lab of Migration and Mobility and/or in the Lab of Population Dynamics and Sustainable Well-Being. 

Digital and computational demography is a growing interdisciplinary field that tackles fundamental questions across all domains of demographic research by combining the methods and perspectives of computational sciences, social and behavioral sciences, and statistics. The field has emerged in parallel with rapid technological improvements in computing, the spread of Internet and mobile technologies, and the increased digitalization of people’s lives. Our group brings together methodologists (from areas like statistics, computer science or formal demography) with experts in various areas of the social sciences in order to foster cross-pollination of ideas, to advance methods and theories of population research, and to address pressing scientific and societal questions.

Candidates who can enrich or complement projects in any Research Area of the Lab of Migration and Mobility or of the Lab of Population Dynamics and Sustainable Well-Being will be considered. Across all profiles, ability and willingness to work in interdisciplinary teams in order to conduct cutting-edge research that advances our understanding of population processes is key.

More at: www.demogr.mpg.de

The diversity of life testifies to proteins’ amazing capacity as chemical tools. They control and drive all the chemi­cal reactions that together are the basis of life. Proteins also function as hormones, signal substances, antibodies and the building blocks of different tissues.

“One of the discoveries being recognised this year concerns the construction of spectacular proteins. The other is about fulfilling a 50-year-old dream: predicting protein structures from their amino acid sequences. Both of these discoveries open up vast possibilities,” says Heiner Linke, Chair of the Nobel Committee for Chemistry.

Proteins generally consist of 20 different amino acids, which can be described as life’s building blocks. In 2003, David Baker succeeded in using these blocks to design a new protein that was unlike any other protein. Since then, his research group has produced one imaginative protein creation after another, including proteins that can be used as pharmaceuticals, vaccines, nanomaterials and tiny sensors.

The second discovery concerns the prediction of protein structures. In proteins, amino acids are linked together in long strings that fold up to make a three-dimensional structure, which is decisive for the protein’s function. Since the 1970s, researchers had tried to predict protein structures from amino acid sequences, but this was notoriously difficult. However, four years ago, there was a stunning breakthrough.

In 2020, Demis Hassabis and John Jumper presented an AI model called AlphaFold2. With its help, they have been able to predict the structure of virtually all the 200 million proteins that researchers have identified. Since their breakthrough, AlphaFold2 has been used by more than two million people from 190 countries. Among a myriad of scientific applications, researchers can now better understand antibiotic resistance and create images of enzymes that can decompose plastic.

Life could not exist without proteins. That we can now predict protein structures and design our own proteins confers the greatest benefit to humankind.

Read the full article at: www.nobelprize.org

The information stored within our chromosomes can be likened to an instruction manual for all cells in our body. Every cell contains the same chromosomes, so every cell contains exactly the same set of genes and exactly the same set of instructions. Yet, different cell types, such as muscle and nerve cells, have very distinct characteristics. How do these differences arise? The answer lies in gene regulation, which allows each cell to select only the relevant instructions. This ensures that only the correct set of genes is active in each cell type.

Victor Ambros and Gary Ruvkun were interested in how different cell types develop. They discovered microRNA, a new class of tiny RNA molecules that play a crucial role in gene regulation. Their groundbreaking discovery revealed a completely new principle of gene regulation that turned out to be essential for multicellular organisms, including humans. It is now known that the human genome codes for over one thousand microRNAs. Their surprising discovery revealed an entirely new dimension to gene regulation. MicroRNAs are proving to be fundamentally important for how organisms develop and function.

Read the full article at: www.nobelprize.org

Complexity and Networks COmmunity and REsources (CORE) is an organization dedicated to connecting scientists and organizations in the fields of Complexity and Network Science. We enhance collaboration among organizations that organize talks, seminars, and events, and we collect all conference and job application deadlines to create a hub where scientists can find relevant news and information. On this page, you will find information about the organizations and contributors involved in this project.

CORE Members: NetPlace, yrCSS, WiNS, Complexity Cat, WWCS, Complexity72h.

More at: complexity-core.github.io

Explore the world of complexity science with NECSI’s Winter Session 2025. This specially designed course offers an in-depth understanding of complex systems, combining theoretical foundations with real-world applications. Whether you aim to deepen your expertise or begin a new intellectual journey, this program provides a comprehensive and academically rigorous exploration of the field.

Course Dates: 10-21 February 2025

Details at: necsi.edu

Do you have a great research idea but need the right people to bring it to life? Help us make Complexity72h an unforgettable experience again by applying to be a tutor in Madrid next June. The call for tutors is now open!

We’re looking for project proposals from experienced researchers to be developed over 72 hours by a team of 6-8 workshop participants.

Deadline for Call for Tutors: January 7th, 2025 

More at: complexity72h.com

When Europeans colonised large parts of the globe, the institutions in those societies changed. This was sometimes dramatic, but did not occur in the same way everywhere. In some places the aim was to exploit the indigenous population and extract resources for the colonisers’ benefit. In others, the colonisers formed inclusive political and economic systems for the long-term benefit of European migrants.

The laureates have shown that one explanation for differences in countries’ prosperity is the societal institutions that were introduced during colonisation. Inclusive institutions were often introduced in countries that were poor when they were colonised, over time resulting in a generally prosperous population. This is an important reason for why former colonies that were once rich are now poor, and vice versa.

Some countries become trapped in a situation with extractive institutions and low economic growth. The introduction of inclusive institutions would create long-term benefits for everyone, but extractive institutions provide short-term gains for the people in power. As long as the political system guarantees they will remain in control, no one will trust their promises of future economic reforms. According to the laureates, this is why no improvement occurs.

However, this inability to make credible promises of positive change can also explain why democratisation sometimes occurs. When there is a threat of revolution, the people in power face a dilemma. They would prefer to remain in power and try to placate the masses by promising economic reforms, but the population are unlikely to believe that they will not return to the old system as soon as the situation settles down. In the end, the only option may be to transfer power and establish democracy.

“Reducing the vast differences in income between countries is one of our time’s greatest challenges. The laureates have demonstrated the importance of societal institutions for achieving this,” says Jakob Svensson, Chair of the Committee for the Prize in Economic Sciences.

Read the full article at: www.nobelprize.org

When we talk about artificial intelligence, we often mean machine learning using artificial neural networks. This technology was originally inspired by the structure of the brain. In an artificial neural network, the brain’s neurons are represented by nodes that have different values. These nodes influence each other through con­nections that can be likened to synapses and which can be made stronger or weaker. The network is trained, for example by developing stronger connections between nodes with simultaneously high values. This year’s laureates have conducted important work with artificial neural networks from the 1980s onward.

John Hopfield invented a network that uses a method for saving and recreating patterns. We can imagine the nodes as pixels. The Hopfield network utilises physics that describes a material’s characteristics due to its atomic spin – a property that makes each atom a tiny magnet. The network as a whole is described in a manner equivalent to the energy in the spin system found in physics, and is trained by finding values for the connections between the nodes so that the saved images have low energy. When the Hopfield network is fed a distorted or incomplete image, it methodically works through the nodes and updates their values so the network’s energy falls. The network thus works stepwise to find the saved image that is most like the imperfect one it was fed with.

Geoffrey Hinton used the Hopfield network as the foundation for a new network that uses a different method: the Boltzmann machine. This can learn to recognise characteristic elements in a given type of data. Hinton used tools from statistical physics, the science of systems built from many similar components. The machine is trained by feeding it examples that are very likely to arise when the machine is run. The Boltzmann machine can be used to classify images or create new examples of the type of pattern on which it was trained. Hinton has built upon this work, helping initiate the current explosive development of machine learning.

Read the full article at: www.nobelprize.org