Nature Computes Better: Opportunity seeds
The Nature Computes Better opportunity space, led by Programme Director Suraj Bramhavar, asks how we can redefine the way computers process information by exploiting principles found ubiquitously in nature.
We’re now funding projects to challenge assumptions, open up new research paths, and provide steps towards new capabilities within the space, with up to £500k each.
270 applications
from across the ecosystem
3 weeks
from application deadline to selection
6 projects
awarded… with more coming soon
Meet the R&D Creators
From unravelling the basis of natural computation in single-celled organisms to demonstrating a commercially viable probabilistic processor, we're funding an array of projects across individual research teams, universities and startups to maximise the chance of breakthroughs.
David Jordan
Independent researcher
Cell Learning for Natural Computing
The project: By combining theory, experiments, and engineering – as well as shifting our thinking from a genome centric to a metabolism centric point of view – David seeks to understand how cells use networks of macromolecules to learn, adapt, and improvise.
The motivation: “Looking to biology for inspiration, to physics for rigour, and to technology for applications has proved to be a very fruitful path to progress. One of the main goals of my research is to understand how biological systems manage to organise themselves such that the natural unfolding of their dynamics, as dictated by the laws of physics, provides computations useful for prediction, adaptation, and control.”
“What excites me the most about the Nature Computes Better thesis is that it promises to focus diverse research on natural systems whose very existence proves that this problem can be solved.”
Shannon Egan, Brock Doiron and Ashraf Lotfi
Deep Science Ventures
Probabilistic Computing with Magnetic Tunnel Junctions
The project: Investigating the controllability of stochastic switching in magnetic tunnel junctions, the team are developing protocols to drive these memory devices as ultra-high-speed, high-quality hardware-based true random number generators.
The motivation: “Modelling probabilistic processes on computers designed for deterministic calculations is highly inefficient, yet the behaviour of the underlying devices is rich with randomness — all we need to do is harness it. Our team is motivated to build a high performance, practical product for true random number generation at-large-scale, simply by getting creative with how we drive known devices and commercial hardware. Coupled with DSV’s established venture building approach, the Nature Computes Better philosophy fits this ambition perfectly.”
Juliane Simmchen and Kimia Witte
University of Strathclyde
(Bio)active Matter Based Computation
The project: Juliane and Kimia are aiming to engineer miniature vehicles that respond to external stimuli, each exhibiting specific behaviours that correspond to symbolic representations.
The motivation: “Beyond simply drawing inspiration from nature to engineer novel technologies, we need to take a step further and begin co-engineering with it.”
“The opportunity space’s vision is so exciting because it requires so-far inexistent connections of completely different areas of science, thinking out of the box, and a big chunk of teamwork.”
Kirsty Wan
University of Exeter
Embodied Cognition in Single Celled Organisms
The project: Kirsty is aiming to unravel the basis of natural computation in single-celled organisms through an interdisciplinary approach combining bioimaging, live-cell experiments, and modelling, to study how single cells compute using physical attributes of its body and the environment.
The motivation: “We can learn so much from how living systems compute at all scales, and there are distinct challenges of ‘being alive’ that make these systems so much more robust and flexible than non-living architectures.”
Phillip Stanley-Marbell
Signaloid
Analog and Digital Representation of Distributions of AI Computations
The project: As large language model workloads scale, their compute requirements are outstripping the capabilities of the substrates on which they run. Phillip asks if architectures capable of natively representing probability distributions could serve as radically more efficient computing substrates for AI workloads.
The motivation: “Computation plays an important role in all aspects of society. There is currently a missed opportunity to use an understanding of the physical world to design efficient computing systems that interact with nature.”
“The Nature Computes Better opportunity space is based on important insights about the current barriers to achieving computing performance and combines these insights with the ambition (and resources) to support bold ideas to surmount these barriers.”
Martin Booth
University of Oxford
Creating Scalable Manufacturing for Optical Computing
The project: Glass is a promising substrate for next-generation optical computing paradigms – Martin is aiming to address a critical aspect of technology deployment, which is the ability to manufacture at scale.
The motivation: “There is an urgent need for technology advancement in this area and there are many impressive ideas about how optical methods can be used to improve future computing technology.”
We'll be announcing more Creators within Nature Computes Better soon
“If you look at most compelling things that started out small and ended up becoming very large, they’re all defined by somebody with a tonne of enthusiasm and energy.”
Suraj Bramhavar, ARIA Programme Director