I am a postgraduate researcher in the fields of nuclear physics and machine learning. My focus is primarily on the study of the method of Neural Quantum States (NQS) and its application to nuclear structure. In a nutshell, NQS uses neural networks as variational ansätze in a search for the ground state of a quantum system. I am very interested in the optimisation of the neural networks used in NQS, and also in the network architectures. My personal goal for my PhD thesis is to reach a deep understanding of the role of symmetries in NQS and use that to find better neural network architectures for nuclear systems. In an ideal world, one could specify all symmetries of a given Hamiltonian and automatically build a neural network that will respect all of them by construction. In my free time, I am always embarked in some personal project that keeps me occupied and in a continuous state of learning.