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Full Time Computational Biophysics Jobs (NOW HIRING)

University of Idaho Job

Postdoctoral Fellow

Moscow, ID

$42K - $57K/yr

Develop spatially explicit computational models using machine learning, hydrologic, and energy ... Analyze large, interdisciplinary datasets spanning biophysical, social, and governance domains

University of Idaho

Postdoctoral Fellow

Moscow, ID · On-site

$42K - $57K/yr

Develop spatially explicit computational models using machine learning, hydrologic, and energy ... Analyze large, interdisciplinary datasets spanning biophysical, social, and governance domains

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As of Jun 11, 2026, the average hourly pay for full time computational biophysics in the United States is $54.93, according to ZipRecruiter salary data. Most workers in this role earn between $46.88 and $73.56 per hour, depending on experience, location, and employer.
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Infographic showing various Full Time Computational Biophysics job openings in the United States as of June 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution, with an average salary of $114,249 per year, or $54.9 per hour.
Postdoctoral Fellow, Structural Biology

Postdoctoral Fellow, Structural Biology

University at Buffalo

Buffalo, NY • On-site

$70K - $85K/yr

Full-time

Posted 2 days ago

University at Buffalo rating

7.2

Company rating: 7.2 out of 10

Based on 28 frontline employees who took The Breakroom Quiz

334th of 535 rated colleges and universities

Job description

Posting Details
Position Information
Fiscal Year
2025-2026
Position Title
Postdoctoral Fellow, Structural Biology
Classification Title
Postdoctoral Associate
Department
Structural Biology Department
Posting Number
R260058
Posting Link
https://www.ubjobs.buffalo.edu/postings/61964
Employer
Research Foundation
Position Type
RF Professional
Job Type
Full-Time
Appointment Term
Salary Grade
E.89
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:
  • 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
Driving Requirements
Salary Range
$70,000 - $85,000
Additional Salary Information
The salary range reflects our good faith and reasonable estimate of the possible compensation at the time of posting, the role and associated responsibilities, and the experience, education, and training of the selected candidate.
Work Hours
37.5 hours per week
Campus
Downtown Campus
Posting Alerts
Special Instructions Summary
Is a background check required for this posting?
No
Background Check Notification
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
References
Number of references required
3
Reference Cutoff Date
Instructions to Applicant

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