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Phd Optimization Research Jobs in Ohio (NOW HIRING)

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... or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research ... Strong foundations in modern machine learning, including deep learning, optimization ...

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... or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research ... Strong foundations in modern machine learning, including deep learning, optimization ...

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... or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research ... Strong foundations in modern machine learning, including deep learning, optimization ...

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... or PhD program in Computer Science, Machine Learning, Statistics, Mathematics, Operations Research ... Strong foundations in modern machine learning, including deep learning, optimization ...

ALLIED MINERAL PRODUCTS

Research Engineer

Columbus, OH · On-site

... optimization of refractory products; travel as required. * Develop and maintain technical ... Master's or PhD preferred. * Minimum of five (5) years of relevant experience in ceramic processing ...

Forschungszentrum Jülich GmbH

$54.76 - $68.45/hr

... for simulating and optimizing decentralized, integrated energy systems. These systems are ... Our research aims to provide scalable, faster‑than‑real‑time capable methods and tools that ...

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Phd Optimization Research information

What is a PhD in optimization research?

A PhD in Optimization Research is an advanced academic degree focused on developing and analyzing mathematical models and algorithms to find the best possible solutions to complex problems. This field often involves linear and nonlinear programming, combinatorial optimization, and stochastic processes, and is applied in areas such as operations research, machine learning, logistics, and engineering. Graduates are prepared for careers in academia, industry, or research institutions, where they work on improving decision-making processes and resource allocation. The program typically involves coursework, comprehensive exams, and original research leading to a dissertation.

What are the typical collaborative projects that a PhD optimization researcher might work on within a multidisciplinary team?

PhD Optimization Researchers often collaborate on projects that integrate expertise from fields such as data science, engineering, computer science, and business analytics. These projects may involve developing and implementing advanced optimization algorithms to solve complex, real-world problems like supply chain management, resource allocation, or energy systems modeling. Team members typically contribute domain knowledge, data, and problem requirements, while the optimization researcher focuses on model formulation, algorithm selection, and solution analysis. Effective communication and adaptability are essential, as researchers must translate technical findings into actionable insights for stakeholders.

What are the key skills and qualifications needed to thrive as a PhD optimization researcher, and why are they important?

To excel as a PhD Optimization Researcher, you typically need a doctorate in applied mathematics, computer science, operations research, or a related field, along with expertise in mathematical modeling and algorithm development. Proficiency with programming languages such as Python, MATLAB, or C++, and familiarity with optimization libraries and tools like Gurobi or CPLEX are commonly required. Strong analytical thinking, creativity, and effective communication skills help in formulating novel solutions and collaborating with interdisciplinary teams. These competencies are crucial for advancing research, solving complex optimization problems, and effectively disseminating findings within both academic and industry settings.

What is the difference between Phd Optimization Research vs Data Scientist?

AspectPhd Optimization ResearchData Scientist
Required CredentialsPhD in Operations Research, Applied Mathematics, or related fieldBachelor's or Master's in Data Science, Computer Science, or related field; some roles prefer PhD
Work EnvironmentResearch labs, academia, R&D departments in industryTech companies, finance, healthcare, consulting firms
Industry UsageFocus on developing optimization algorithms, mathematical modelingFocus on data analysis, machine learning, predictive modeling
Common Search/ComparisonYesYes

While both roles involve advanced analytical skills, Phd Optimization Research primarily focuses on developing and refining optimization algorithms and mathematical models, often in research or academic settings. Data Scientists analyze large datasets to extract insights and build predictive models, often applying machine learning techniques. The roles overlap in data analysis and quantitative skills but differ in their core focus and typical work environments.

What are popular job titles related to Phd Optimization Research jobs in Ohio?

For Phd Optimization Research jobs in Ohio, the most frequently searched job titles are:

What job categories do people searching Phd Optimization Research jobs in Ohio look for?

The top searched job categories for Phd Optimization Research jobs in Ohio are:

What cities in Ohio are hiring for Phd Optimization Research jobs?

Cities in Ohio with the most Phd Optimization Research job openings:

Phd Optimization Research jobs near you
Infographic showing various Phd Optimization Research job openings in Ohio as of August 2026, with employment types broken down into 1% As Needed, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution.

Materials Informatics Research Scientist

Riverside Research

Dayton, OH

$130K - $220K/yr

Full-time

Re-posted 29 days ago

Job description

Riverside Overview

Riverside Research is an independent National Security Nonprofit dedicated to research and development in the national interest. We provide high-end technical services, research and development, and prototype solutions to some of the country’s most challenging technical problems. All Riverside Research opportunities require U.S. Citizenship.

Position Overview

Riverside Research is seeking a highly skilled and innovative Materials Informatics Research Scientist to join our advanced materials team. The successful candidate will utilize high-performance computing (HPC) and data-driven approaches to accelerate the discovery, design, and optimization of advanced composite materials. Research, develop, test, prototype, demonstrate and transition HPC and data-driven technologies that support the performance prediction of advanced composites materials in aerospace. This role involves working closely with interdisciplinary teams to apply computational methods and data analytics to solve complex material science problems.

Responsibilities

  • Lead the technical direction with government and team, and mentor junior/mid-level staff.
  • Develop and implement high-performance computing models and simulations to study the behavior and performance of advanced composite materials.
  • Utilize data-driven techniques, including machine learning and artificial intelligence to analyze large datasets and extract meaningful insights for material design.
  • Collaborate with material scientists, engineers, and data scientists to integrate computational and experimental data for comprehensive material understanding.
  • Perform multi-scale modeling and simulation to link microstructural features with macroscopic properties.
  • Design and execute computational experiments to predict material properties and guide the development of new composite materials.
  • Analyze simulation and experimental data to validate model and improve their predicative accuracy.
  • Prepare technical reports, presentations, and publications to communicate research findings.

Qualifications

Required Qualifications:

  • Bachelor’s degree in materials science, computational science, electrical engineering, or a related field with a focus on high-performance computing and data-driven research with 8 years of experience or 6 years with MS or 3 years with PhD.
  • Must be eligible to obtain a Top Secret security clearance.
  • 5+ years of experience in computational modeling and simulation of composite materials.
  • Proven experience leading a technical project and team.
  • Strong knowledge of high-performance computing platforms and software, such as MPI, OpenMP. CUDA, and related tools.
  • Proficiency in data analytics, machine learning, and artificial intelligence techniques.
  • Experience with multi-scale modeling and integration of different simulation methods.
  • Excellent analytical and problem-solving skills with the ability to interpret complex data.
  • Experience in collaborative research environment, working effectively with cross-functional teams.

Desired Qualifications:

  • Experience in the aerospace, automotive, or renewable energy industries.
  • Familiarity with materials characterization techniques and their integration with computational models.
  • Experience with grant writing and securing funding for research projects.
  • Capability to develop projects using a mixture of python, MATLAB, C, and data processing languages.

Global Comp

$130,000 - $220,000 This represents the typical compensation range for this position based on experience, location and other factors.

Closing Statement

Riverside Research Institute is a not-for-profit, technology-oriented defense company, where service to our customers and support of our staff is our overall mission. Riverside is an affirmative action-equal opportunity employer and complies with all applicable federal, state, and local laws regarding recruitment and hiring. Riverside offers comprehensive compensation and benefit packages to our employees. Riverside bases its employment decisions solely on technical experience, qualifications and other job-related criteria related to our organizational purpose as a not-for-profit company, and without regard to race, color, religion, age, sex marital status, sexual orientation, national origin, physical or mental disability, veteran’s status or any other status legally protected by applicable federal, state, and local law.

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