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graphic representation of the research project
Traditionally, experiments and molecular modelling are used to measure the properties of many materials to find the most suitable material for a desired function, for example to catalyse a chemical reaction with high efficiency. Instead, here machine learning algorithms will be developed that can learn from experimental and simulation data and predict optimal materials given the desired properties.

The UvA Data Science Centre seeks to accelerate data-driven research within the University of Amsterdam. As part of its mission to foster interdisciplinary research, it recently announced the funding of seven PhD positions researching new data science methods to tackle challenging problems in specific domains.

The PhD project awarded in the chemistry domain focuses on the development of catalytic materials that enable the use of CO2 as a building block for high value-added chemicals and fuels - instead of treating it as a waste. In an Inverse Design approach, novel machine learning approaches will be developed (e.g. deep generative modelling, deep probabilistic programming) that are able to infer optimal catalyst materials and process conditions given a set of desired chemical and process properties. 

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