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Computational Structural Biology Jobs in Texas (NOW HIRING)

MD Anderson

Postdoctoral Fellow - Genomic Medicine

Houston, TX · On-site +1

$46K - $63K/yr

... biological structural data are encouraged to apply. A strong computational background is required. Expertise with PyTorch and torch-geometric, or equivalent technologies, is essential. Experience ...

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How much do computational structural biology jobs pay per hour?

As of Aug 30, 2026, the average hourly pay for computational structural biology in Texas is $36.17, according to ZipRecruiter salary data. Most workers in this role earn between $26.43 and $42.70 per hour, depending on experience, location, and employer.

What is computational structural biology?

Computational structural biology is a field that uses computer-based methods and simulations to study the three-dimensional structures of biological molecules, such as proteins and nucleic acids. Researchers use computational tools to predict molecular structures, analyze their dynamics, and understand how they interact with other biomolecules. This approach complements experimental techniques like X-ray crystallography and cryo-electron microscopy, helping scientists gain insights into biological function and design drugs or therapies. The field integrates knowledge from biology, chemistry, physics, and computer science.

What are the key skills and qualifications needed to thrive as a computational structural biologist?

To thrive as a Computational Structural Biologist, you need a solid background in structural biology, molecular modeling, and bioinformatics, typically supported by a PhD or relevant advanced degree. Proficiency in programming languages (such as Python or R), molecular visualization tools (like PyMOL or Chimera), and experience with structural databases are commonly required. Strong analytical thinking, collaboration, and communication skills help you interpret complex data and work effectively within multidisciplinary teams. These competencies are crucial for advancing scientific understanding and developing novel therapeutics in the field of structural biology.

What are some common challenges faced by professionals in computational structural biology when collaborating with experimental scientists?

One common challenge is effectively communicating complex computational findings to experimental colleagues who may not be familiar with certain modeling techniques. Additionally, aligning computational predictions with experimental data can require iterative discussions to ensure that models are biologically relevant and experimentally testable. Successful collaboration often relies on building mutual understanding of each team's methodologies and limitations, which fosters productive interdisciplinary research.

What are the most commonly searched types of Computational Structural Biology jobs in Texas?

The most popular types of Computational Structural Biology jobs in Texas are:

What are popular job titles related to Computational Structural Biology jobs in Texas?

For Computational Structural Biology jobs in Texas, the most frequently searched job titles are:

What job categories do people searching Computational Structural Biology jobs in Texas look for?

The top searched job categories for Computational Structural Biology jobs in Texas are:

Computational Structural Biology jobs near you
Infographic showing various Computational Structural Biology job openings in Texas as of August 2026, with employment types broken down into 90% Full Time, 7% Part Time, 2% Contract, and 1% Nights. Highlights an 88% Physical, 3% Hybrid, and 9% Remote job distribution, with an average salary of $75,232 per year, or $36.2 per hour.

Senior AI Scientist Drug Discovery & Computational Biology

Vytwo

Dallas, TX • On-site

$180 - $240/hr

Other

Posted 24 days ago

Job description

Position Title | Senior AI Scientist – Drug Discovery & Computational Biology

Domain EXP | Healthcare

Location | Boston, MA - Hybrid

Duration | C2C

Must Have | Drug Discovery & Computational Biology

Job Description

We are seeking a highly skilled professional with deep expertise in applying Artificial Intelligence and Machine Learning to drug discovery and development. The ideal candidate will have experience in:

  • AI and machine learning methodologies for drug discovery, including predictive modeling, lead optimization, and translational research applications.
  • Graph machine learning techniques for biological networks, molecular property prediction, target identification, and knowledge graph-based discovery.
  • Virtual cell screening and AI-driven target discovery platforms, with particular focus on rare disease research and therapeutic innovation.
  • AI-driven molecular design for small molecules and biologics (Abs/VHH), including toxicity prediction, off-target assessment, developability analysis, and candidate optimization.
  • Structural biology, including protein structure analysis, molecular interactions, computational modeling, and integration of structural data into drug discovery workflows.

The candidate should demonstrate a strong track record of leveraging advanced computational approaches to accelerate therapeutic discovery and possess the ability to collaborate effectively with multidisciplinary research and development teams.

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