Responsibilities
- Design, implement and deliver performant and scalable algorithms based on state-of-the-art machine learning, deep learning and bioinformatics methodologies using distributed computing systems (CPUs, GPUs, TPUs, Cloud, etc.).
- Write high-quality, maintainable, and modular code together with precise documentation and tests.
- Report and present development progress and research insights, both internally and externally.
- Follow and communicate the latest developments in machine learning and cryo-EM/ET.
Requirements
- Master, PhD degree or equivalent experience (previous work) in applied mathematics, physics, computer science, or a related scientific field.
- Experience in machine learning and deep learning techniques demonstrated via previous work, publications or contributions to open source projects.
- Software engineering proficiency and experience in writing and reviewing Python code.
- Experience using deep learning frameworks such as Jax or PyTorch and related scientific libraries.
- Knowledge of linear algebra, calculus and statistics.
- Proficiency with Git workflow and development with best coding practices.
- Excellent communication skills in English.
Nice-to-haves
- Experience with cryo-EM/ET reconstruction algorithms.
- Familiarity with modern topics in geometric deep learning, representation learning and diffusion.
- Experience with Rust or C++ to support high performance tooling adjacent to our machine learning code.
- Proven ability to contribute to open-source and/or research communities, including publishing scientific papers at conferences (NeurIPS, ICLR, ICML, etc.).
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