About the Role:
We are on a quest to find a pioneering engineer to join our Computational Biology group, a team at the forefront of developing single-cell and spatial genomic assays. This role is crucial for advancing our in-situ platforms and assays, which have revolutionized how researchers explore complex biological systems across various fields such as cancer biology, immunology, and neuroscience. The ideal candidate will possess a deep understanding of statistical modeling, numerical optimization algorithms, and the practical application of these skills, striking the right balance between classical and modern approaches. You should be a self-starter, capable of independent thinking, and thrive in a highly collaborative, interdisciplinary, and fast-paced environment. You have experience working on large code repositories.
What You Will Be Doing:
- Implementing signal processing, pattern matching, and model fitting pipelines to analyze large multidimensional and multimodal datasets to improve our spatial and in-situ assays.
- Creating analytical models or simulations of the data generating process to elucidate and communicate the trade-offs involved in system design, data quality and algorithms.
- Optimizing algorithms leveraging CPU/GPU architecture to enhance data processing capabilities.
To Be Successful in This Role, You Will Need:
- PhD, MS, or equivalent industry experience in a quantitative field such as computational biology, computer science, physics, mathematics, statistics, or electrical engineering.
- Proficient programming skills in Python and compiled languages (e.g. C/C++, Rust, etc), with a solid background in deploying code on GPUs.
- The ability to research, adapt, and innovate upon existing methods from literature and libraries, transforming them into well-architected, efficient code.
Additional Desirable Skills to Have:
- Exposure to multiplexed imaging and sequencing technologies, and spatial biology.
- A creative problem-solving approach that prioritizes extracting maximum performance from code, while navigating the imperfections inherent in real-world data and maintaining theoretical integrity.
- Knowledge of sequence alignment models, information theory, inverse problems.
This role is tailored for an individual who is not only technically proficient but also possesses a strategic mindset, capable of driving projects forward in a dynamic research and development landscape. If you are passionate about leveraging computational power to unlock the mysteries of biology at an unprecedented scale, we encourage you to apply.
