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Physics Based Machine Learning Jobs in Delaware (NOW HIRING)

Celanese

Digital Innovation Engineer

Wilmington, DE · On-site

Proficiency in AI + physics-based machine learning. * Working understanding of material science fundamentals * Strong foundation in applied statistics, experimental design, and probabilistic modeling.

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Physics Based Machine Learning information

What types of projects or problems does a Physics Based Machine Learning professional typically work on?

Physics Based Machine Learning professionals often work on projects that involve applying machine learning techniques to physical systems, such as improving simulations in engineering, optimizing energy systems, or accelerating scientific research through data-driven modeling. Daily tasks might include developing algorithms that incorporate physical laws, analyzing simulation data, and collaborating with experts from engineering, data science, or research teams. The role can involve both theoretical and hands-on work, often requiring iterative testing and validation. This environment provides opportunities to tackle cutting-edge challenges, contribute to innovation, and potentially lead to career paths in research, product development, or advanced analytics.

What is a Physics Based Machine Learning job?

A Physics Based Machine Learning job involves developing machine learning models that incorporate physical laws and domain knowledge to improve predictions and interpretability. Professionals in this field work at the intersection of physics, data science, and artificial intelligence to create models that are more robust, generalizable, and efficient, especially in scientific and engineering applications. Responsibilities often include data analysis, algorithm development, numerical simulations, and integrating physics-based constraints into ML models. These roles are common in industries like climate science, robotics, materials science, and computational physics.

What are the key skills and qualifications needed to thrive in the Physics Based Machine Learning position, and why are they important?

To thrive in Physics Based Machine Learning, you need advanced knowledge of physics, strong programming skills (Python, MATLAB, or C++), and a deep understanding of machine learning and statistical modeling, typically supported by a master's or PhD in physics, engineering, or a related field. Familiarity with simulation software, scientific computing libraries (such as TensorFlow, PyTorch, NumPy), and version control systems is essential. Strong problem-solving ability, effective communication, and cross-disciplinary collaboration skills set outstanding candidates apart. These competencies are crucial for designing robust, real-world models that integrate physical principles with data-driven techniques to solve complex problems.

What are popular job titles related to Physics Based Machine Learning jobs in Delaware? For Physics Based Machine Learning jobs in Delaware, the most frequently searched job titles are:
What job categories do people searching Physics Based Machine Learning jobs in Delaware look for? The top searched job categories for Physics Based Machine Learning jobs in Delaware are:
What cities in Delaware are hiring for Physics Based Machine Learning jobs? Cities in Delaware with the most Physics Based Machine Learning job openings:
Physics Based Machine Learning jobs near you

Digital Innovation Engineer

Celanese International Corporation

Wilmington, DE • Hybrid

Full-time

Posted 6 days ago

Job description

Celanese Engineered Materials is seeking an Engineer, Digital Innovation – Predictive Modeling & Advanced Experimentation role. This role is a specialized technical position focused on applying AI + Physics into predictive modeling, experimental design, and Bayesian optimization to enable faster, more confident decisions in new product and material development.

This role is an opportunity to play a key role in advancing predictive modeling and advanced experimental strategy to accelerate the design and development of nextgeneration materials. Applying rigorous quantitative methods to enable informed decisionmaking early in technology and product development.

The role operates at the intersection of modeling, statistics, and machine learning, with a strong emphasis on translating these capabilities into practical approaches that support technology and innovation programs. This position also builds and deploys digital methods to guide experimentation, prediction, and optimization that support computer aided engineering and new product development efforts.

**Location can be hybrid in one of the following locations: 

  • Wilmington, DE
  • Florence, KY
  • Auburn Hills, MI
  • Irving, TX

Predictive Modeling for Material Property Design

  • Develop and apply predictive and hybrid machine learning approaches for the prediction of properties key to designing the next generation of materials.
  • Integrate mechanistic understanding, statistical modeling, and datadriven methods to generate reliable, decisionready predictions.
  • Quantify model confidence and limitations to support riskaware technical decisions.
  • Translate complex modeling outputs into clear, actionable insights for technology and innovation stakeholders.

Experimental Design & Bayesian Optimization for New Product Development

  • Design and apply advanced experimental design strategies and Bayesian optimization for new product development.
  • Efficiently explore highdimensional design spaces to prioritize experiments and identify optimal candidates for laboratory evaluation.
  • Apply adaptive and sequential learning approaches to balance exploration and exploitation under limited data conditions.

  • Master's Degree or higher, or with equivalent experience in computer science, computer engineering, machine learning, physics, applied mathematics or related field
  • Understanding of advanced materials, chemical processes, and laboratory data is a plus.
  • 1+ years' work experience with modeling development, data analysis, business communication, and digital transformation is highly desirable.
  • Proficiency in AI + physics-based machine learning.
  • Working understanding of material science fundamentals
  • Strong foundation in applied statistics, experimental design, and probabilistic modeling.
  • Expertise in predictive modeling and simulation for material or system property prediction.
  • Experience with uncertainty quantification, model validation, and decision support under uncertainty.
  • Ability to translate advanced quantitative methods into practical workflows including proof-of-concept full-stack (backend + frontend) applications that inform technology and product decisions.
  • Working across the full lifecycle: problem formulation → model and strategy development → application and adoption.
  • Communicating complex modeling and experimental concepts clearly to diverse technical audiences.
  • Influencing technology and innovation decisions through quantitative, modeldriven insight.
  • Operating effectively in crossfunctional environments spanning product development, technology, innovation, and digital teams.

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