The European Research Council (ERC) has awarded Consolidator Grants to neurologist Matthijs Brouwer and physicist Philippe Corboz. The prestigious grants are awarded to individual researchers and amount to around 2 million euros per project.
Consolidator Grants are intended for researchers who obtained their PhDs 7 to 12 years ago. The grants enable researchers to consolidate independent positions within their fields.
Matthijs Brouwer (Amsterdam UMC, Amsterdam Neuroscience): Improving Prognosis by Using Innovative Methods to Diagnose Causes of Encephalitis
Encephalitis is a severe inflammation of the brain that can be caused by bacteria, viruses, fungi and parasites, as well as by overactivity of the immune system (autoimmune diseases). In half of the people with encephalitis the cause remains unclear, and it is therefore difficult for doctors to choose the right treatment. One in six patients with encephalitis dies, which is partly due to the treatment delay caused by the uncertainty about the cause of the problem.
Brouwer will be searching for new causes of encephalitis in a large group of patients. He will use innovative techniques to study cerebrospinal fluid in order to identify previously unknown viruses and other pathogens. By looking at patterns of gene expression (RNA), lipids, metabolites and proteins in cerebrospinal fluid, he wants to find a fingerprint of each cause of encephalitis, allowing for the cause to be determined within a few hours. This will enable fast and targeted treatment, and will thus improve the prognosis of patients with encephalitis.
Philippe Corboz (Institute of Physics): State-of-the-Art Simulations of Quantum Many-Body Systems with the Next-Generation Tensor Network Algorithms
Elementary building blocks at the smallest scales – atoms, molecules or even smaller elementary particles – sometimes cause effects that are only visible at much larger scales of materials. This effect is known in physics as emergence. Understanding the emergent phenomena in quantum mechanical many-body systems with strong interactions is one of the key challenges for physicists. A famous example is superconductivity at high temperatures (where ‘high’ is still 100-200 degrees below zero on the Celsius scale), an effect which was discovered experimentally more than 30 years ago, but for which the detailed explanation is still one of the biggest puzzles in physics.
Another prominent example is systems of interacting particles with spin, relevant for certain classes of materials that show a variety of magnetic phenomena. The same systems give rise to novel exotic states of matter called quantum spin liquids. The accurate study of all these systems is in general very challenging, in particular because standard numerical approaches often fail. In recent years, substantial progress has been achieved with numerical approaches based on ideas from quantum information theory: the ‘tensor network methods’ in the title of Corboz’s proposal. The goal of his project is to develop the next generation of tensor network methods, and to use these methods to shed new light on all sorts of challenging open problems, including high-temperature superconductivity, quantum spin liquids, and other novel states of matter.