Julia Valter is a PhD student in Spatial Statistics at Chalmers University of Technology, focused on bridging point process theory and machine learning through "point process learning." With 11 years of experience across research, teaching, and industry internships, she combines strong mathematical foundations (top grades and exchange at ETH Zürich) with practical ML work—ranging from anomaly detection with LSTMs/Transformers at CERN to PyTorch projects at Ericsson. Julia has presented invited talks across Europe, co-authored peer-reviewed work, and helped organize conferences while supervising BSc/MSc projects, showing both academic leadership and mentorship. Her unusual blend of functional-programming experience (resource grammar implementation) and hands-on ML on Kubernetes highlights a rare mix of theoretical depth and engineering fluency. Based in Gothenburg, she actively cultivates international collaborations and applies statistical rigor to real-world spatial problems like forestry, astronomy, and emergency services.
11 years of coding experience
Doctor of Philosophy - PhD, Mathematical Statistics and Probability, Doctor of Philosophy - PhD, Mathematical Statistics and Probability at Chalmers tekniska högskola
Exchange studies, Mathematics, Exchange studies, Mathematics at ETH Zürich
Mathematics, 21.9/22.5, Mathematics, 21.9/22.5 at Hvitfeldtska Gymnasiet
Contributions:50 commits, 36 pushes in 1 year 8 months
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