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Scientific Machine Learning Jobs in Bryan, TX (NOW HIRING)

Required qualifications: -Bachelor's degree in forestry, natural resources, spatial sciences, math ... machine learning and advanced statistical techniques. Opportunities: -Grow professionally by ...

... transfer, machine design, manufacturing processes, and control systems. Ability to explain free ... learning science to create personalized learning experiences. Through 1-on-1 Online Tutoring ...

... of scientific instruments and research apparatus. In this hands-on position, you'll work closely ... The ideal candidate will bring extensive machine shop experience, strong proficiency in SolidWorks ...

Statics Tutor

College Station, TX · Remote

$18 - $40/hr

... machines, centroids, moments of inertia, friction, and distributed forces. Ability to explain ... learning science to create personalized learning experiences. Through 1-on-1 Online Tutoring ...

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Scientific Machine Learning information

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$12

$29

$48

How much do scientific machine learning jobs pay per hour?

As of Aug 26, 2026, the average hourly pay for scientific machine learning in Bryan, TX is $29.02, according to ZipRecruiter salary data. Most workers in this role earn between $17.74 and $37.02 per hour, depending on experience, location, and employer.

What is scientific machine learning?

Scientific machine learning (SciML) is an interdisciplinary field that combines principles from machine learning and scientific computing to solve complex scientific and engineering problems. It involves developing algorithms and models that can learn from data and physical laws, such as differential equations, to make predictions, optimize systems, or gain insights into phenomena. SciML is widely used in areas like physics, biology, climate science, and engineering, enabling researchers to accelerate simulations and make data-driven discoveries. The field often leverages both traditional numerical methods and modern machine learning techniques, making it a rapidly evolving area of research.

What are the key skills and qualifications needed to thrive as a scientific machine learning professional, and why are they important?

To thrive as a Scientific Machine Learning professional, you need a strong background in mathematics, statistics, programming (often Python), and domain-specific scientific knowledge, typically with a graduate degree in a STEM field. Proficiency in machine learning frameworks (such as TensorFlow or PyTorch), scientific computing tools (like NumPy, SciPy), and experience with high-performance computing are commonly required. Critical thinking, problem-solving, and collaborative communication are vital soft skills for designing experiments and interpreting complex data. These skills ensure robust, reproducible results and the ability to bridge scientific inquiry with advanced computational methods.

What are some common challenges faced by professionals in scientific machine learning, and how can they be addressed?

Professionals in Scientific Machine Learning often encounter challenges such as integrating domain-specific scientific knowledge with machine learning models, managing large and complex datasets, and ensuring that models are interpretable and physically consistent. Collaboration with domain experts and interdisciplinary teams is essential to bridge knowledge gaps and validate results. To address these challenges, it is helpful to invest time in understanding the underlying scientific principles, keep up-to-date with advancements in both machine learning and scientific fields, and utilize specialized tools and frameworks designed for scientific data.

What is the difference between Scientific Machine Learning vs Data Scientist?

AspectScientific Machine LearningData Scientist
Required credentialsAdvanced degrees in CS, ML, or related fields; knowledge of scientific computingDegree in CS, statistics, or related fields; strong analytical skills
Work environmentResearch labs, academia, industry R&D teamsBusiness analytics, tech companies, consulting firms
Industry usageResearch, scientific computing, engineering simulationsBusiness insights, predictive modeling, data analysis

Scientific Machine Learning focuses on integrating scientific knowledge with machine learning techniques for research and engineering applications. Data Scientists analyze data to extract insights and build predictive models for business or operational purposes. While both roles require strong technical skills, Scientific Machine Learning emphasizes scientific computing and domain-specific modeling, whereas Data Scientists focus on data analysis and visualization.

What job categories do people searching Scientific Machine Learning jobs in Bryan, TX look for?

The top searched job categories for Scientific Machine Learning jobs in Bryan, TX are:

What cities near Bryan, TX are hiring for Scientific Machine Learning jobs?

Cities near Bryan, TX with the most Scientific Machine Learning job openings:

Tenure Track/Tenured: Open Rank (Quantitative & Computational Methods)

College Station, TX • On-site


Texas A&M University
Colleges, Universities, and Professional Schools • 1 - 5K employees

7.8

Company rating: 7.8 out of 10

Based on 146 frontline employees who took The Breakroom Quiz

232nd of 622 rated colleges and universities

Great coworkers

People enjoy working here

Good employer


Full-time

Posted 13 days ago


Job description

The Department of Psychological and Brain Sciences, College of Arts and Sciences, at Texas A&M University invites applications for 3 full-time positions at the rank of Assistant Professor (tenure-track), Associate Professor (tenured), or Professor (tenured) in quantitative and/or computational methods with 9-month academic appointments to begin in Fall 2027. Associate Professor and Professor candidates will be considered for tenure review upon hire.
We seek applicants whose research advances quantitative and/or computational methods in psychological science. Areas of interest include (but are not limited to): psychometrics, computational modeling, machine learning and artificial intelligence applications, multilevel and longitudinal methods, biostatistics, network analysis, and data science. The goal of this cluster hire is to build long-term departmental strength in quantitative and/or computational methods in psychological science.
The successful candidates will engage in research, grant applications, and funded projects, teach courses in our graduate and undergraduate programs, and act as engaged citizens of the department, college, university, and their field. We seek candidates who have demonstrated or show the potential to develop and maintain an active, productive, and impactful research program and to develop and enhance graduate training in quantitative and/or computational methods. Typical teaching loads for the department are 3 courses per year, distributed across undergraduate and graduate programs. Evidence of teaching effectiveness is a plus.
The Department of Psychological and Brain Sciences at Texas A&M University is a community of scholars committed to generating scientific discoveries in the discipline, providing rigorous and inspiring undergraduate and graduate education, and engaging in outreach about psychology and the application of psychological science. We value collaboration, with many faculty having grants and research projects with colleagues inside and beyond the department. The Department has numerous programs to support early career faculty, including a formal mentoring program.
We currently offer doctoral programs in clinical psychology, cognition, and cognitive neuroscience, social and personality psychology, industrial-organizational psychology, and behavioral and cellular neuroscience, and a master's program in industrial-organizational psychology. We currently have 60 full-time faculty, over 100 PhD students, and approximately 2,000 undergraduate majors. Texas A&M University is a land, sea, and space grant institution that holds the distinction of classification as an R1 Doctoral University (highest research activity), and faculty benefit from the resources and support associated with this designation. Our department is committed to broadening participation in higher education, and has a policy of being responsive to the needs of dual-career couples. The Department is interested in candidates who, through their research, teaching, and/or service, will contribute to the breadth and excellence of the academic community, and well as the educational needs of the population of Texas and the global community.
Texas A&M University is a Top 20 public research institution and among the largest universities in the US. Located in College Station, the university is 90 miles from Houston, 100 miles from Austin, and 165 miles from Dallas. The Bryan-College Station metropolitan area has over 260,000 residents and is experiencing rapid job growth. Texas has no state income tax. College Station has a local airport served by American Airlines.
Qualifications
A PhD in Psychology or a closely related field is required. Preference will be given to candidates who have demonstrated or demonstrate the potential to publish high quality impactful research that advances quantitative and/or computational methods in psychological science, writing and obtaining grants, and engaging in high quality teaching and mentoring activities in quantitative and/or computational methods.
Application Instructions
Applicants should submit: a cover letter; CV; a personal statement to include philosophy and plans for research, teaching and service (as applicable); and contact information for at least three references. To apply, please visit: https://apply.interfolio.com/190514 For questions and additional assistance, please contact Jennifer Fraustro at jenfrosty12@tamu.edu .
Priority will be given to applications received by October 1, but applications will be accepted until the position is filled.
Application Process
This institution is using Interfolio's Faculty Search to conduct this search. Applicants to this position receive a free Dossier account and can send all application materials, including confidential letters of recommendation, free of charge.
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