Talia Kimber is a data scientist with eight years of experience applying machine learning to real-world problems across government, defense, and pharmaceutical research. She holds a PhD focused on machine learning for drug discovery and combines deep learning expertise (PyTorch, TensorFlow/Keras) with strong statistical and mathematical foundations. Talia has led R&D projects quantifying operational capabilities for the Swiss Armed Forces and is now shaping AI strategy and modernizing the legislative consultation process for the Canton of Fribourg. Her background in molecular ML and practical experience with data augmentation and heterogeneous data integration give her a rare ability to move models from research into decision-ready production. Colleagues describe her as a cross-disciplinary collaborator who pairs rigorous research with pragmatic software development to deliver measurable impact.
8 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy - PhD, Machine learning for drug discovery., Doctor of Philosophy - PhD, Machine learning for drug discovery. at Freie Universität Berlin
Master's degree, Statistics, Master's degree, Statistics at University of Geneva
Mathematics, Mathematics at Ecole polytechnique fédérale de Lausanne
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