AI Engineer, AI+CryoET

What you'll do

• Develop deep learning models to detect and localize gold nanoparticles and macromolecular particles in cryoET data.
• Create methods to improve tomogram reconstruction by leveraging gold nanoparticle detections to address alignment and noise issues.
• Design and execute rigorous training pipelines for 3D image analysis, including handling of missing wedge artifacts and CTF effects.
• Develop supervised and self-supervised models using both simulated and annotated experimental cryoET data.
• Identify necessary human annotation points and design guided efforts to improve model accuracy.
• Maintain a well-documented codebase to ensure the reproducibility and expansion of research results.
• Contribute to scientific publications and present findings at professional conferences.

What we're looking for

• Master's or PhD in Computer Science, Applied Mathematics, Physics, Computational Chemistry, or a related field.
• 3+ years of experience training and evaluating deep learning models on 3D or volumetric image data.
• Proficiency in Python with expertise in PyTorch and/or JAX.
• Experience with detection, segmentation, or inverse problems in imaging.
• Ability to reason about neural network behavior from first principles including architecture, regularization, and training procedures.
• Experience with scalable GPU-based computing on Linux HPC clusters and high-throughput processing for large-scale data.
• Strong skills in rigorous experimental design, including model comparisons, ablation studies, and reproducibility.
• Excellent communication skills and ability to work effectively in an interdisciplinary environment.