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the architecture of the human brain. This project aims to co-develop algorithms for neuromorphic hardware, focusing on the inherent stochasticity in neuromorphic materials. This will pave the way towards solutions
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-develop algorithms for neuromorphic hardware, focusing on the inherent stochasticity in neuromorphic materials. This will pave the way towards solutions for problems in optimisation and sampling, creating
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that can be used at speeds that are orders of magnitude faster than current CMOS technology. In order to demonstrate the computational power of so-called synchronised stochasticity for approximate Bayesian
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geometry and topology, number theory, functional analysis, mathematical physics, non-commutative geometry, special functions and applied stochastics. The Department of Mathematics has a friendly and