June 2021 – Present

ETH Zürich (IWF) / Inspire AG (Zurich, Switzerland)

  • Application of machine learning techniques to develop physical models on electric discharge machining and for machine control and prognostics.
  • Management of R&D collaboration projects between the university and industrial partners.
  • Teaching support for different engineering courses.


July 2019 – May 2021

Microsoft Research Quantum (Redmond, US / Zurich, Switzerland)

  • Document and support the Microsoft’s quantum programming language Q# and the platform Azure Quantum.
  • Contribute to open source projects on quantum computing. (e.g. Q# libraries, randomness generators).


PhD in Mechanical and Process Engineering

2023 - Present

ETH Zürich - Inspire AG (Zurich, Switzerland)

My thesis aims to apply learning based methods to optimize the control of Wire EDM and Die-Sinking EDM machines. The focus is on improving efficiency, speed, and accuracy over traditional methods like PIDs.

MSc in Physics

2018 – 2020

ETH Zürich (Zurich, Switzerland)
Focused on:

  • Quantum information: theory and implementations
  • Machine Learning
  • Quantum Field Theory and Gravitation

Master Thesis: Extracting physical parameters from an environment using AI-agents

  • Abstract: Development of a new architecture combining different reinforcement learning agents together with deep learning techniques to create agents capable of designing experiments to gain physical insight.
  • Supervisors: Raban Iten, Renato Renner

BSc in Physics

2014 – 2018
Universidad Autónoma de Madrid (Madrid, Spain)

Theoretical BSc Thesis: Quantum violation of Leggett-Garg inequalities in condensed matter systems

Experimental BSc Thesis: Entropy waves in super-fluid helium-IV