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Postdoctoral Fellow
Postdoctoral Fellow, Structural Biology Full-time
Postdoctoral Fellow, Structural Biology
Position Information
Position Title: Postdoctoral Fellow, Structural Biology
Department: Structural Biology Department
Posting Link: [Please click the Apply button for the link or address]
Job Type: Full-Time
Posting Detail Information
Position Summary
The Grant Lab at the University at Buffalo is seeking a highly skilled Postdoctoral Fellowto lead the computational development of a novel generative AI framework for structural biology. This project sits at the intersection of X-ray scattering and deep learning, aimed at integrating experimental data to predict protein ensemble structures. As an Empire AI-funded fellow, you will have early access to the Empire AI clusters, utilizing state-of-the-art GPU architectures to push the boundaries of structural biology.
This position is a prestigious Empire AI Fellowship at the University at Buffalo, designed for a “CS-first†researcher to drive the technical evolution and large-scale implementation of a new platform for protein structure prediction. Working at the intersection of generative AI and biophysics, the Fellow will focus on expanding the current framework to model dynamic protein ensembles. As an Empire AI-funded fellow, you will have access to the Empire AI Alpha and Beta clusters, utilizing hundreds of state-of-the-art GPUs (including H100 and GB200 nodes) for scaling generative models for structural biology.
The Postdoctoral Fellow will be responsible for:
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- Technical Expansion: Implementing the next phase of the project to transition from rigid-body models to sophisticated systems for protein ensemble modeling.
- Computational Optimization: Resolving hardware-specific performance and numerical precision challenges across diverse GPU environments.
- Architecture Design: Leading the design of new loss functions, model architectures, and synthetic datasets that integrate experimental X-ray scattering data.
- System Stability: Ensuring numerical reproducibility and stability in large-scale distributed training workloads.
Learn more:
- Our benefits, where we prioritize your well-being and success to enhance every aspect of your life.
- Being a part of the University at Buffalo community.
As an Equal Opportunity / Affirmative Action employer, the Research Foundation will not discriminate in its employment practices due to an applicant’s race, color, religion, sex, sexual orientation, gender identity, national origin and veteran or disability status.
Minimum Qualifications
- Doctoral degree or equivalent in Computer Science, Data Science, Computational Physics, or a related field with a focus on Deep Learning.
- All degree requirements, including dissertation, must be completed by the start date.
- Expert-level proficiency in PyTorch and/or JAX.
- Demonstrated experience with Distributed Training (e.g., DeepSpeed, FSDP) and managing large-scale GPU workloads.
- Strong understanding of low-level model stability, including mixed-precision training (BF16/FP8) and gradient accumulation.
Preferred Qualifications
- At least two years of experience beyond the PhD in a research or engineering environment focused on large-scale AI.
- Experience with geometric deep learning, diffusion architectures, or related frameworks (e.g., OpenFold, AlphaFold2/3).
- Familiarity with Docker/Singularity for reproducible HPC environments.
- Experience with CUDA-level optimization or debugging hardware-specific performance differences.
- Basic knowledge of protein structure, folding, or biophysics.
Physical Demands
Salary Range
$70,000 – $85,000
Special Instructions Summary
Is a background check required for this posting?
No
Contact Information
Contact’s Name: Thomas Grant
Contact’s Pronouns:
Contact’s Title: Assistant Professor
Contact’s Email: tdgrant@buffalo.edu
Contact’s Phone: 716-829-5490
Posting Dates
Posted: 04/10/2026
Deadline for Applicants: Open Until Filled
Date to be filled:
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