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Physics Informed Machine Learning Jobs in Delaware

Celanese

Digital Innovation Engineer

Wilmington, DE · On-site

Applying rigorous quantitative methods to enable informed decision‑making early in technology and ... Proficiency in AI + physics-based machine learning. * Working understanding of material science ...

OneMain Financial

Data Science Analyst

Wilmington, DE · Hybrid

Apply diverse statistical and machine learning techniques to analyze a variety of datasets to solve ... Biostatistics, Physics, Economics, Operations Research, or Computer Science) * Outstanding ...

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What is a Physics Informed Machine Learning job?

A Physics Informed Machine Learning (PIML) job involves developing AI models that integrate physics-based principles to improve accuracy, interpretability, and generalization. Professionals in this role use machine learning techniques alongside domain knowledge in physics, engineering, or applied sciences to solve complex problems in areas like fluid dynamics, materials science, and climate modeling. Responsibilities often include designing algorithms, implementing simulations, and validating results against experimental or real-world data. Employers typically seek expertise in deep learning, numerical methods, and programming languages like Python.

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

To thrive in Physics Informed Machine Learning, you need a solid background in physics, strong mathematical and statistical skills, and experience with machine learning algorithms, typically supported by an advanced degree in a relevant field. Proficiency with programming languages like Python, frameworks such as TensorFlow or PyTorch, and familiarity with numerical simulation tools are commonly required. Effective problem-solving, clear communication, and the ability to collaborate with interdisciplinary teams make a significant impact in this role. These capabilities are essential for developing robust, interpretable machine learning models that leverage physical laws to solve complex, real-world problems.

What are the typical challenges faced by professionals working in Physics Informed Machine Learning roles?

Professionals in Physics Informed Machine Learning often encounter challenges integrating complex physical theories with advanced machine learning models, requiring deep domain knowledge and strong technical skills. Balancing model accuracy with computational efficiency and ensuring that models are both interpretable and generalizable can be demanding. Collaboration with domain experts, data scientists, and engineers is common, as projects often span multiple disciplines. Successfully navigating these challenges provides valuable experience and is highly regarded, often leading to further career advancement in research, engineering, or leadership positions.
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Digital Innovation Engineer

Digital Innovation Engineer

Celanese Corporation

Wilmington, DE • On-site

Full-time

Posted 8 days ago

Celanese rating

7.6

Company rating: 7.6 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

48th of 88 rated chemical manufacturers

Job description

Overview:
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 next-generation materials. Applying rigorous quantitative methods to enable informed decision-making 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

Responsibilities:
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 data-driven methods to generate reliable, decision-ready predictions.
  • Quantify model confidence and limitations to support risk-aware 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 high-dimensional 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.

Qualifications:
  • 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, model-driven insight.
  • Operating effectively in cross-functional environments spanning product development, technology, innovation, and digital teams.

What Celanese employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom

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About Celanese

Sourced by ZipRecruiter

We produce products that make our lives a little easier, by helping customers to bring their inspired ideas and innovations to life. From the global production network of our Acetyl Chain, we provide materials that are critical to the global chemicals and paints and coatings industries. From our broad portfolio of Materials Solutions, we advance automotive and consumer electronic designs and enable life-improving medical, food and beverage products – we offer solutions to our customers to help them succeed.

Industry

Chemical manufacturing

Company size

5,001 - 10,000 Employees

Headquarters location

Irving, TX, US

Year founded

1912

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