Vaidotas Šimkus
Member Of Technical Staff at Orbital
Vilnius, Vilnius County, Lithuania
Summary
👤
Senior🎓
Top SchoolVaidotas Šimkus is an ML researcher and Member of Technical Staff at Orbital, specializing in generative and atomistic force-field models to accelerate materials discovery for applications like carbon capture and data center cooling. With 11 years of engineering and research experience and a PhD in Machine Learning from the University of Edinburgh, he bridges probabilistic methods and practical ML systems. His background spans industry and academia—from optimizing neural inference at Disney Research to Bayesian experimental design consulting for pharmaceutics—bringing both production-grade software skills and deep statistical insight. An active open-source contributor, he has improved the cross-platform Joplin note-taking app through full-stack work in Electron and React Native, reflecting a knack for shipping robust, user-facing features. Based in Vilnius, he favors simple, effective solutions that turn complex scientific problems into deployable tools.
11 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Machine Learning, Doctor of Philosophy - PhD, Machine Learning at The University of EdinburghBachelor of Engineering - BE, Computer Software Engineering, First Class Honours, Bachelor of Engineering - BE, Computer Software Engineering, First Class Honours at University of SouthamptonHigh School, Lithuanian Matura, High School, Lithuanian Matura at Kaunas University of Technology Gymnasium
English, Lithuanian
Github Skills (8)
nodejs10
javascript10
react-native10
electron10
react9
webdav4
dropbox3
android3
Programming languages (7)
Github contributions (5)
laurent22/joplin
Jan 2020 - Jul 2020
Joplin - the privacy-focused note taking app with sync capabilities for Windows, macOS, Linux, Android and iOS.
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:5 commits, 7 PRs, 45 comments in 6 months
Contributions summary:Vaidotas contributed to the Joplin note-taking application by implementing features and fixing bugs related to the desktop and React Native clients. Their work included refactoring code for improved compatibility, adding features for hierarchical tags and handling API calls, and addressing issues related to file export and sandboxing. The user demonstrated expertise in JavaScript, Electron, and React Native, contributing to both the client-side and shared code base.
enex-filesevernoteonedriveiosdropbox
vsimkus/pytorch
Jan 2020 - Oct 2020
Tensors and Dynamic neural networks in Python with strong GPU acceleration
Contributions:176 pushes, 5 branches in 8 months
pythongpu-accelerationdeep-learninggpuacceleration
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