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Robust Optimization Academic Jobs (NOW HIRING)

Loyola University

Senior Data Scientist

Chicago, IL · On-site

... g., resource optimization, academic program review). * Data Engineering & Pipeline Development * Build, optimize, and maintain robust data pipelines that support analytics, dashboards, and ...

Nrel

ML and Optimization Engineer

Golden, CO · On-site

... academia, and other national laboratories. We offer robust professional development opportunities, and a competitive benefits package designed to support your career and well-being. The National ...

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All JobsRobust Optimization Academic Jobs

Robust Optimization Academic information

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$24.5K

$91.9K

$162.5K

How much do robust optimization academic jobs pay per year?

As of Jun 4, 2026, the average yearly pay for robust optimization academic in the United States is $91,879.00, according to ZipRecruiter salary data. Most workers in this role earn between $73,500.00 and $109,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Robust Optimization Academic, and why are they important?

To thrive as a Robust Optimization Academic, you need a strong background in mathematics, optimization theory, and research methodology, usually supported by a PhD in a related field. Familiarity with mathematical programming software (such as MATLAB, Gurobi, or CPLEX) and experience publishing in peer-reviewed journals are typically required. Analytical thinking, problem-solving, and effective communication are important soft skills for collaborating with peers and teaching students. These competencies are vital for advancing research, securing academic funding, and contributing to the development of robust optimization theory and applications.

What are common challenges faced by Robust Optimization Academics when working on interdisciplinary research projects?

Robust Optimization Academics often collaborate with experts from fields such as engineering, economics, and computer science. A common challenge in these interdisciplinary projects is bridging knowledge gaps and aligning objectives across different domains. Effective communication, flexibility in approach, and a willingness to learn from collaborators are crucial for success. Additionally, integrating robust optimization techniques into practical, real-world systems can require significant adaptation and validation efforts.

What is a Robust Optimization Academic?

A Robust Optimization Academic is a researcher or professor who specializes in the field of robust optimization, which focuses on developing mathematical models and algorithms that deliver reliable solutions under uncertainty. They often work in universities or research institutions, conducting theoretical studies, publishing papers, and teaching courses related to optimization and operations research. Their research aims to address real-world problems where data may be uncertain or subject to change, ensuring solutions remain effective across a range of scenarios.

What is robust optimization?

Robust optimization is a mathematical approach used in operations research and decision-making to find solutions that remain effective under uncertain conditions. It involves modeling uncertainties explicitly and designing strategies that perform well across a range of possible scenarios, often requiring specialized tools and programming skills.

What is the difference between Robust Optimization Academic vs Data Scientist?

AspectRobust Optimization AcademicData Scientist
Required CredentialsAdvanced degrees in operations research, mathematics, or related fieldsBachelor's or master's in data science, statistics, or computer science
Work EnvironmentResearch-focused, academic institutions, or industry R&D teamsBusiness environments, tech companies, or consulting firms
Industry UsageOptimization research, academia, and specialized industry applicationsData analysis, predictive modeling, and decision-making support
Common Search/ComparisonRobust Optimization Academic vs Data Scientist

Robust Optimization Academics focus on theoretical research and developing new optimization models, often within academic or research settings. Data Scientists apply data analysis and modeling techniques to solve practical business problems. While both roles involve quantitative skills, their work environments and objectives differ significantly.

Infographic showing various Robust Optimization Academic job openings in the United States as of May 2026, with employment types broken down into 1% As Needed, 86% Full Time, 7% Part Time, and 6% Contract. Highlights an 91% Physical, 3% Hybrid, and 6% Remote job distribution, with an average salary of $91,879 per year, or $44.2 per hour.

Machine Learning - Engineer & Researcher

StaffRight Associates, LLC

Manhattan, NY • On-site

Other

Posted 26 days ago

Job description

Preface

This search targets the intersection of Computational Biophysics and Foundational Machine Learning, necessitating a candidate who possesses an elite academic pedigree, typically characterized by a Ph.D. in Computer Science, Physics, or a related quantitative field. The role demands a first-principles mastery of deep learning architectures to navigate the high-dimensional complexity of molecular space. To succeed, the researcher must bridge abstract algorithmic theory with practical biochemical application, synthesizing novel neural frameworks that can effectively model and predict the stochastic behavior of biological macromolecules. This mandate requires an individual capable of translating rigorous mathematical proofs into scalable, high-performance computational models that drive systemic breakthroughs in therapeutic discovery.

The Mission

StaffRight Associates is recruiting to identify a visionary Machine Learning - Engineer | Researcher to join an elite interdisciplinary collective in New York City. The mission is to architect and deploy sophisticated ML frameworks that redefine the boundaries of biomolecular simulation and drug design. By integrating advanced deep learning techniques with massive-scale computational power, the successful incumbent will play a pivotal role in transforming the predictive accuracy of molecular dynamics and accelerating the evolution of medicinal chemistry through systemic algorithmic innovation.

Core Technical Objectives

  • Synthesize novel deep learning architectures—including graph neural networks, generative models, and reinforcement learning frameworks—to decode complex biophysical interactions.

  • Engineer high-performance Python-based environments to facilitate the training and deployment of models on bespoke, ultra-high-speed supercomputing infrastructure.

  • Validate the efficacy of neural networks in enhancing the precision of quantum chemical models and structural biology simulations.

  • Optimize generative algorithms to autonomously design and refine molecular structures with high therapeutic potential.

  • Decouple complex biological datasets into actionable features, leveraging transfer learning and deep belief networks to inform the drug discovery pipeline.

  • Orchestrate collaborative research efforts alongside chemists and biologists to ensure mathematical models align with empirical scientific reality.

Candidate DNA

  • Architectural Philosophy: A deep-seated commitment to developing robust, scalable, and innovative deep learning solutions for multi-dimensional scientific challenges.

  • Technical Depth: Mastery of the deep learning stack, including but not limited to CNNs, RNNs, Boltzmann machines, and graph-based learning.

  • Algorithmic Versatility: The ability to pivot between various domains such as cheminformatics, medicinal chemistry, and quantum mechanics with intellectual curiosity and technical rigor.

  • Systemic Impact: A proven track record of pioneering ML research or engineering that has resulted in peer-reviewed publications or significant industry advancements.

  • Coding Proficiency: Expert-level Python capabilities, with a preference for candidates who exhibit a sophisticated understanding of software engineering principles and performance optimization.

Academic & Research Pedigree

  • Educational Foundation: An advanced degree (Ph.D. or Master’s) in a STEM discipline with a heavy emphasis on computational methods, mathematics, or theoretical physics.

  • Research Excellence: Demonstrated history of innovation in machine learning, evidenced by a portfolio of work that showcases an ability to solve non-trivial, open-ended scientific problems.

  • Mathematical Rigor: A first-principles understanding of the statistical and mathematical underpinnings of modern AI/ML.

Partnering with StaffRight Associates

At StaffRight Associates, we operate at the intersection of technical synthesis and structural alignment. We don’t just match resumes to keywords; we map your engineering DNA, your architectural philosophy, your approach to system resilience, and your Goal-Execution-Mapping, to the most sophisticated STEM challenges in the industry.

When you partner with us, you are engaging with a team that speaks your language and understands the nuances of high-stakes innovation. We are committed to placing elite talent where their technical contributions drive systemic impact.

Apply