Develop Python-based middleware that receives experimental directives from a Bayesian optimization orchestrator and translates them into coordinated PLC commands across multiple instruments.
Develop Python-based middleware that receives experimental directives from a Bayesian optimization orchestrator and translates them into coordinated PLC commands across multiple instruments.
Develop Python-based middleware that receives experimental directives from a Bayesian optimization orchestrator and translates them into coordinated PLC commands across multiple instruments.
Develop Python-based middleware that receives experimental directives from a Bayesian optimization orchestrator and translates them into coordinated PLC commands across multiple instruments.
Develop Python-based middleware that receives experimental directives from a Bayesian optimization orchestrator and translates them into coordinated PLC commands across multiple instruments.
Develop Python-based middleware that receives experimental directives from a Bayesian optimization orchestrator and translates them into coordinated PLC commands across multiple instruments.
Mathematical optimization * Linear optimization concepts * Nonlinear continuous optimization ... Bayesian concepts * Hypotheses testing Education requirements Graduate degree in Applied ...
Mathematical optimization * Linear optimization concepts * Nonlinear continuous optimization ... Bayesian concepts * Hypotheses testing Education requirements Graduate degree in Applied ...
Mathematical optimization * Linear optimization concepts * Nonlinear continuous optimization ... Bayesian concepts * Hypotheses testing Education requirements Graduate degree in Applied ...
Mathematical optimization * Linear optimization concepts * Nonlinear continuous optimization ... Bayesian concepts * Hypotheses testing Education requirements Graduate degree in Applied ...
Mathematical optimization * Linear optimization concepts * Nonlinear continuous optimization ... Bayesian concepts * Hypotheses testing Education requirements Graduate degree in Applied ...
Mathematical optimization * Linear optimization concepts * Nonlinear continuous optimization ... Bayesian concepts * Hypotheses testing Education requirements Graduate degree in Applied ...
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New
Bayesian Optimization information
What is the difference between Bayesian Optimization vs Data Scientist?
| Aspect | Bayesian Optimization | Data Scientist |
|---|---|---|
| Primary Focus | Optimizing complex functions and hyperparameters | Analyzing data, building models, deriving insights |
| Required Skills | Statistics, probability, machine learning, programming | Statistics, programming, data analysis, visualization |
| Work Environment | Research labs, AI/ML teams, R&D departments | Business, tech companies, consulting firms |
| Common Tools | Python, R, Bayesian libraries (e.g., GPy, scikit-optimize) | Python, R, SQL, visualization tools |
Bayesian Optimization is a specialized technique used within machine learning and AI to efficiently tune hyperparameters or optimize functions. Data Scientists often utilize Bayesian Optimization as part of their toolkit but have broader responsibilities, including data analysis, modeling, and reporting. While Bayesian Optimization focuses on optimization tasks, Data Scientists work on understanding and interpreting data to inform business decisions.

Full-time
Medical, Dental, Vision, Life, Retirement, PTO
Posted 8 days ago
Texas A&M University rating
7.8
Based on 146 frontline employees who took The Breakroom Quiz
228th of 616 rated colleges and universities
Job description
Job Title
Automation & Controls EngineerAgency
Texas A&M EngineeringDepartment
Materials Science & EngineeringProposed Minimum Salary
CommensurateJob Location
College Station, TexasJob Type
StaffJob Description
Why work for Texas A&M Engineering?
Who we are
Engineering has been part of Texas A&M University since its opening in 1876 as the Agricultural and Mechanical College of Texas. Today, the College of Engineering is the largest college on the College Station campus with more than 25,000 engineering students enrolled in 15 departments. Its mission is to serve Texas, the nation and the global community by providing engineering graduates who are well-founded in engineering fundamentals, instilled with the highest standards of professional and ethical behavior, and prepared to meet the complex technical challenges of society.
As the research arm of Engineering, the Texas A&M Engineering Experiment Station (TEES) is a state agency within the Texas A&M University System with a mission to improve lives through basic and applied engineering research, workforce development and technology transition. Our collaborations with industry, academia and government provide cutting-edge solutions to global technical challenges.
We are deeply committed to recruiting and retaining a talented workforce that embraces our core values of Respect, Excellence, Leadership, Loyalty, Integrity, and Service, by offering competitive salaries, an array of benefits, an extensive support network, and above all, an enriching and highly collaborative working community that is deeply passionate about our vision for higher education, research, and public service.
Job Description
Purpose:
We are seeking an Automation & Controls Engineer to join our research group and lead the automation and integration of metallurgical processing equipment into a closed-loop, AI-orchestrated experimental platform. The successful candidate will connect arc melting, hot rolling, mechanical testing, and robotic sample transfer systems into a coordinated workflow driven by a Bayesian optimization engine - translating algorithmic experimental directives into machine actions and returning structured results to the decision-making system.
Responsibilities:
- Automate vacuum arc melting operations: electrode positioning, chamber sequencing, power profile control, and ingot extraction.
- Automate hot rolling workflows: pass schedule execution, temperature monitoring, thickness feedback, and inter-pass timing.
- Automate mechanical testing routines: specimen loading, test execution, and data capture on universal testing machines, hardness testers, and related equipment.
- Design and commission robotic sample transfer systems to move specimens between processing, characterization, and testing stations without manual intervention.
- Develop Python-based middleware that receives experimental directives from a Bayesian optimization orchestrator and translates them into coordinated PLC commands across multiple instruments.
- Implement real-time data acquisition from sensors (thermocouples, load cells, encoders, imaging systems) and structure outputs into machine-readable formats for the AI/ML pipeline.
- Design[EM1] safety interlocks, error recovery protocols, and fail-safe routines for unattended operation of high-temperature and high-force equipment.
- Diagnose and resolve issues across the full stack - electrical, mechanical,[EM2] and software.
What we need:
- Bachelor's degree in applicable field or equivalent combination of education and experience.
- six years of related experience.
What is helpful:
- BS or MS in Electrical Engineering, Mechanical Engineering, Computer Engineering, Mechatronics, or a related field
- Experience with PLC programming (Allen-Bradley, Siemens, or equivalent) and HMI development
- Hands-on experience interfacing with scientific, industrial, or manufacturing equipment
- Experience with robotic systems (UR, FANUC, or similar) and motion control
- Prior experience in a research laboratory, national lab, or metals processing environment
- Exposure to computer vision for automated inspection, sample identification, or quality control
- Experience with high-temperature or high-force equipment (furnaces, rolling mills, mechanical test frames)
Knowledge, Skills and Abilities:
- Proficiency in Python for instrument control, scripting, and automation
- Ability to read and create electrical schematics and wiring diagrams
- Strong troubleshooting skills across hardware and software domains
- Familiarity with ROS/ROS2, LabVIEW, or data acquisition frameworks
- Understanding of AI/ML workflow integration - ability to design APIs and data pipelines that connect automated instruments to optimization and decision-making algorithms
- Familiarity with digital twins or simulation-based testing of automation sequences
Work Location:
- Main Campus - College Station, TX.
About Materials Science and Engineering
The Department of Materials Science and Engineering's mission is to educate global leaders in materials science and engineering; perform research to address materials challenges of the 21st century; impact society, industry, national security, and the environment and attract the best minds and develop them into the leaders of tomorrow. Despite being one of the youngest departments in the College of Engineering at Texas A&M University, it is one of the largest materials science and engineering departments in the country. Our goal is to be the largest one while providing excellent education to the students.
Texas A&M Engineering provides an outstanding benefits package including but not limited to:
- Competitive medical insurance benefits through Blue Cross and Blue Shield of Texas and Prescription coverage by Express Scripts.
- Options for Vision, Dental, Life, and Long-Term Disability insurance.
- A defined benefit retirement plan with the Teacher Retirement System of Texas (TRS) with 8.25% employer contribution.
- Additional Voluntary Retirement Programs: Tax Deferred Account 403(b) and a Deferred Compensation Program 457(b).
- Flexible spending account options for medical and childcare expenses
- Generous paid time off with holidays, vacation and sick leave.
- Robust free training access through LinkedIn Learning plus professional development opportunities.
- Tuition assistance and Educational release time to further your academic pursuits.
- Access to Engineer Your Wellness programs that provide opportunities for employees to engage in health and fitness.
- Wellness release time offered to employees to promote work/life balance.
Helpful Applicant Information
Required Materials for Application:
- Resume/CV
- Three work references with their contact information; at least one reference should be from a supervisor/former supervisor.
- Letter of interest (dependent upon position)
Applications received by Texas A&M Engineering must have all required job application data entered.
Failure to provide all job application data could result in an invalid submission and a rejected application.
Compensation Philosophy:
- Recruit and retain high-performing workforce through competitive compensation and career development including career pathing, coaching and skills development.
- Offer total rewards, flexible benefits, professional development, performance recognition and work-life balance.
- Ensure resources are readily available and utilized to attract and retain the highest quality researchers, faculty, and staff.
Employment Eligibility Verification
- If hired, you will be required to complete the federal Employment Eligibility Verification I-9 form. You will be required to present original and unexpired documents to prove your identity and authorization to work in the United States. Documents need to be presented no later than the third day of employment.Failure to do so will result in delay of start date.
All positions are security-sensitive. Applicants are subject to a criminal history investigation, and employment is contingent upon the institution's verification of credentials and/or other information required by the institution's procedures, including the completion of the criminal history check.
Equal Opportunity/Veterans/Disability Employer.
What Texas A&M University employees say
Pay
Benefits
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Workplace
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About Texas A&M AgriLife
Sourced by ZipRecruiter
Industry
Colleges, universities, and professional schools
Company size
1,001 - 5,000 Employees
Headquarters location
College Station, TX, US
Year founded
1914