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Ai For Science information

How does collaboration typically work between AI for Science professionals and domain experts in research teams?

AI for Science professionals frequently work closely with experts in fields such as biology, chemistry, or physics to identify scientific problems that can benefit from machine learning techniques. Collaboration usually involves regular meetings to translate complex scientific challenges into data-driven models, sharing domain knowledge, and iteratively refining solutions. Effective communication and a willingness to bridge gaps between computational and scientific perspectives are essential. This interdisciplinary teamwork not only enhances the impact of AI solutions but also fosters ongoing learning and innovation.

What is AI for Science?

AI for Science refers to the application of artificial intelligence and machine learning techniques to accelerate scientific discovery and research. By leveraging large datasets, complex models, and advanced computational methods, AI helps scientists analyze data, identify patterns, simulate experiments, and make predictions across various scientific fields such as biology, chemistry, physics, and climate science. This approach can significantly speed up research, uncover new insights, and solve problems that were previously too complex or time-consuming for traditional methods.

Which AI for science is best?

The best AI tools for science depend on the specific application, such as data analysis, modeling, or simulation. Popular options include TensorFlow, PyTorch, and specialized platforms like DeepMind or IBM Watson, which are used by researchers to develop and deploy AI models in scientific research. Proficiency in programming languages like Python and understanding of machine learning concepts are essential for roles in AI for science.

What are the key skills and qualifications needed to thrive as an AI for Science specialist, and why are they important?

To thrive as an AI for Science Specialist, you need a strong background in computer science, mathematics, and scientific domains, often supported by advanced degrees (e.g., PhD or MSc) in relevant fields. Proficiency with machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools, and familiarity with high-performance computing environments are typically required. Critical thinking, interdisciplinary collaboration, and effective communication are crucial soft skills for translating scientific problems into AI solutions. These skills are vital for developing innovative models, ensuring research rigor, and enabling impactful scientific discoveries.

What is the difference between Ai For Science vs Data Scientist?

AspectAi For ScienceData Scientist
Required CredentialsDegree in Science, Computer Science, or related fields; knowledge of AI and machine learningDegree in Statistics, Computer Science, or related fields; strong programming skills
Work EnvironmentResearch labs, scientific institutions, tech companies focused on scientific applicationsCorporate, tech firms, finance, healthcare, and other industries analyzing data
Industry UsageApplied to scientific research, simulations, and experimental data analysisUsed for data analysis, predictive modeling, and business insights

Ai For Science focuses on applying AI techniques to scientific research and experiments, often requiring a background in science and specialized knowledge of AI. Data Scientists analyze large datasets across various industries to extract insights and build models. While both roles involve AI and data analysis, Ai For Science is more research-oriented within scientific contexts, whereas Data Scientists work across diverse sectors on data-driven decision making.

What are popular job titles related to Ai For Science jobs in Texas? For Ai For Science jobs in Texas, the most frequently searched job titles are:
What job categories do people searching Ai For Science jobs in Texas look for? The top searched job categories for Ai For Science jobs in Texas are:
What cities in Texas are hiring for Ai For Science jobs? Cities in Texas with the most Ai For Science job openings:
Ai For Science jobs near you
Infographic showing various Ai For Science job openings in Texas as of August 2026, with employment types broken down into 1% Internship, 1% As Needed, 72% Full Time, 23% Part Time, and 3% Contract. Highlights an 76% Physical, 3% Hybrid, and 21% Remote job distribution.

Postdoctoral Associate - AI for Brain Tumors

Baylor College of Medicine

Houston, TX • On-site

$62K/yr

Full-time

Re-posted 14 days ago

Baylor College of Medicine rating

8.0

Company rating: 8.0 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

184th of 616 rated colleges and universities

Job description

Postdoctoral Associate - AI for Brain Tumors
Division: Neurosurgery
Work Arrangement: Onsite only
Location: Houston, TX
Salary Range: $62,232
FLSA Status: Exempt
Work Schedule: Monday - Friday, 8 a.m. - 5 p.m.
Summary
The Postdoctoral Associate will develop next-generation AI models for large-scale perturbation modeling in brain tumors. The project will involve building and applying state-of-the-art machine learning approaches, including foundation models, variational autoencoders (VAEs), and transformer-based architectures, to integrate single-cell and multi-omic datasets. The goal is to decode tumor cellular heterogeneity and tumor microenvironment interactions, and to identify targetable genes, pathways, and therapeutic strategies at single-cell resolution.
Baylor College of Medicine typically follows similar to the NIH stipulated stipend guidelines for Postdoctoral Associates.
Job Duties
  • Develops and implements AI models for perturbation prediction:
    • Designs, trains, and evaluates machine learning models (e.g., transformer-based architectures, VAEs, and foundation models) to predict cellular responses to genetic and pharmacologic perturbations. This includes preprocessing large-scale single-cell and multi-omic datasets, defining model architectures, optimizing training pipelines on GPU clusters, and benchmarking against existing methods.
  • Integrate and analyze large-scale single-cell and multi-omic:
    • Processes and harmonizes scRNA-seq, scATAC-seq, and related datasets across brain tumor cohorts.
    • Performs downstream analyses such as cell state annotation, pathway enrichment, and tumor-tumor microenvironment interaction modeling to generate biologically meaningful insights.
  • Leads computational research projects and method development.
  • Performs other job-related duties as assigned.

Minimum Qualifications
  • MD or Ph.D. in Basic Science, Health Science, or a related field.
  • No experience required.

Preferred Qualifications
  • Ph.D. in Computational Biology, Bioinformatics, Computer Science or a related quantitative field.
  • Strong background in machine learning and statistical modeling, with experience in deep learning frameworks (e.g., PyTorch or TensorFlow). Familiarity with modern architectures such as transformers, variational autoencoders (VAEs), and foundation models is highly desirable.
  • Experience in analyzing large-scale genomics or single-cell datasets (e.g., scRNA-seq, scATAC-seq).
  • Proficiency in Python and experience with R/Seurat or Scanpy.
  • Strong skills in writing efficient, reproducible, and well-documented code.
  • Evidence of productivity through first-author publications or preprints in computational biology, machine learning, or related fields.

Baylor College of Medicine is an Equal Opportunity/Affirmative Action/Equal Access Employer.
PD; SN
Requisition ID: 24929

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