Open Positions

SAIL regularly offers new proposal for Master students interested in writing their thesis on topic related to artificial intelligence, algorithms, and strategy. If you are interested in any of these theses please send an email to yshrestha@ethz.ch

Examining the evolution of science with automated causal relationship detection from text

Causal relationships form an important outcome of most scientific work. Automatic identification of causal relationships from texts could provide an important methodological impethus to study the dynamics of science. To this end, in this study we aim to draw on methods for extracting causal relationships from texts to draw a causal map for scientific knowledge in a particular domain, and understand its evolutions in socio-economic contexts.

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Deep Learning on Text Documents and Knowledge Graphs (in collaboration with Stanford University)

In this project we will evaluate some existing deep-learning methods and develop new techniques to learn embeddings from text documents and knowledge graphs. Especifically, the goal is to extract information from text documents, so that they can be populated into knowledge bases. Working on this project requires experience with both structure and unstructured datasets, and of the mathemetical models to represent them.

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Organisations and Algorithms

Firms can benefit from partially/fully automated decision-making because (in certain scenarios) it reduces coordination costs and frees up human attention. However, there is a need to rethink organisational designs when integrating these predictive technologies into our organisations. The focus of this thesis should aim to understand how organisations and organisation designs are being shaped by algorithms as tools and agents.

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