Many real-world phenomena are successfully modelled with partial differential equations and any such equation becomes a stochastic partial differential equation, if its coefficients, initial and boundary conditions and/or forcing terms are uncertain/random. Important applications of stochastic partial differential equations are in the areas of interacting particle systems, nonlinear filtering, fluid dynamics, climate modelling, financial mathematics, neuroscience, to name just a few. The development of numerical methods for stochastic partial differential equations is a relatively young research area and has emerged only during the last two decades. In general, the availability of efficient, robust and reliable simulation algorithms is a necessary prerequisite for a widespread implementation of stochastic partial differential models in science and engineering. The workshop is thus devoted to the development and analysis of numerical methods for stochastic partial differential equations and their applications. It will cover topics ranging from strong and weak approximation, multi-level Monte-Carlo or approximation of invariant measures to geometric numerical methods and dynamical issues for stochastic partial differential equations.