Jakob Thrane

Lead Machine Learning Engineer at TDC NET

Copenhagen, Capital Region of Denmark
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Summary

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Senior
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Top School
Jakob Thrane is a Lead Machine Learning Engineer based in Denmark with nine years of experience bridging academic research and production AI systems, including a PhD focused on applying deep learning to next-generation mobile communications. He has led ML-driven digital twin and GIS workflows, built large-scale medical imaging pipelines for a 1.2M x-ray dataset, and architected enterprise-grade data orchestration using Dagster and MLflow. Comfortable across Python, PyTorch, Golang and systems profiling, Jakob combines hands-on optimization of CPU/GPU bottlenecks with pragmatic software engineering to move models from research to near-real-time production. Notably, his background spans both telecom R&D (including a visiting role at Nokia Bell Labs) and startup CTO/architect experience, giving him a rare mix of domain depth and delivery-focused leadership.
code9 years of coding experience
job8 years of employment as a software developer
bookTelecommunication, Telecommunication at Huawei Telecom Seeds For The Future
bookMaster's Degree, Telecommunications Engineering, Master's Degree, Telecommunications Engineering at Danmarks Tekniske Universitet
bookBachelor of Science (BS), Telecommunication engineering, Bachelor of Science (BS), Telecommunication engineering at Universidade Federal do Espírito Santo
bookTechnical University of Denmark
languagesEnglish, German, Danish
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Github Skills (39)

simulation10
operation10
toolbox10
lte10
matlab10
metadata10
loss10
airbyte9
data-science9
dagster9
observation9
analytics9
scheduler9
python9
prediction9

Programming languages (5)

TypeScriptDockerfileHTMLMATLABPython

Github contributions (5)

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Sonohi/monster

Feb 2017 - May 2020

MONSTeR is a framework built around the LTE system toolbox available in Matlab
Contributions:2 releases, 363 commits, 66 PRs in 3 years 3 months
ltematlabtoolboxsimulation
jakthra/PseudoRayTracingOSM

Feb 2020 - Aug 2020

Conv nets applied to OSM maps for path loss prediction
Contributions:22 commits, 18 pushes, 3 branches in 6 months
openstreetmapconvpredictionnetsloss
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