AI Engineer - AI+CryoET
Core
Develop AI methods for 3D particle detection, localization, and structural analysis in cryo-electron tomography (cryoET) data to study chromatin organization and synaptic molecular targets.
Role type
Senior IC machine-learning engineer (cryoET/structural biology)
Builds
Deep learning models for particle detection, tomogram reconstruction improvement, and nucleosome arrangement identification in cryoET datasets.
Domain
Computational structural biology / Cryo-electron tomography / Machine Learning
Deliverable
production ML models
Required skills
Deep learning model training and evaluation, 3D/volumetric image analysis, Python, PyTorch or JAX, GPU-based computing on Linux HPC clusters, experimental design and reproducibility, sim-to-real transfer
Preferred skills
Cryo-EM/ET data processing and tomographic reconstruction, molecular dynamics simulations, differentiable rendering or neural radiance fields, template matching and particle picking
Technologies
PyTorch, JAX, OpenMM, LAMMPS, IMOD, Warp, RELION, AreTomo, MRC, Zarr
Responsibilities
Develop and evaluate deep learning models for detecting gold nanoparticles and macromolecular particles in cryoET data; Design and execute AI model training pipelines handling missing wedge artifacts and CTF effects; Guide human annotation efforts for model improvement; Contribute to scientific publications and maintain documented codebases; Collaborate with interdisciplinary teams across multiple institutions.
Seniority
Senior, hands-on IC
