Weak gravitational lensing, the distortion of the images of high-redshift galaxies due to foreground matter structures on large scales, is one
of the most promising tools of cosmology to probe the dark sector of the Universe. The statistical analysis of lensing distortions can reveal
the dark-matter distribution on large scales, The European space satellite Euclid will measure cosmological parameters to unprecedented accuracy. To achieve this ambitious goal, a number of sources of systematic errors have to be quanti?ed and understood. One of the main origins of bias is related to the detection of galaxies. There is a strong dependence on local number density and whether the galaxy's light emission overlaps with nearby
objects. If not handled correctly, such ``blended`` galaxies will strongly bias any subsequent measurement of weak-lensing image
distortions.
The goal of this PhD is to quantify and correct weak-lensing detection biases, in particular due to blending. To that end, modern machine-
and deep-learning algorithms, including auto-di?erentiation techniques, will be used. Those techniques allow for a very e?cient estimation
of the sensitivity of biases to galaxy and survey properties without the need to create a vast number of simulations. The student will carry out cosmological parameter inference of Euclid weak-lensing data. Bias corrections developed during this thesis will be included a prior in galaxy shape measurements, or a posterior as nuisance parameters. This will lead to measurements of cosmological parameters with an reliability and robustness required for precision cosmology.