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We develop a novel Bayesian level set approach for geometric inverse problems that arise in PDE-constrained applications.Our work consists of a rigorous application of the infinite-dimensional Bayesian framework whereby proving the measurability of the observational map that arises from our level-set representation enables us to show existence and well-posedness of the posterior measure.