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Physics Informed Machine Learning Jobs in Pennsylvania

SIG Susquehanna

Faculty Fellow

Bala Cynwyd, PA · On-site

$80 - $100/hr

... machine learning at scale to uncover actionable insights - driving data-informed decisions from ... physics, applied mathematics, or related fields. * Exceptional newly minted PhDs or postdocs ...

New

The Pennsylvania State University

Machine Learning/Deep Learning techniques. Education and Experience: A PhD in physics, astronomy, or a closely related field must be completed before the position begins. Additional information:

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Physics Informed Machine Learning information

What is a physics informed machine learning?

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 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.

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 popular job titles related to Physics Informed Machine Learning jobs in Pennsylvania?

For Physics Informed Machine Learning jobs in Pennsylvania, the most frequently searched job titles are:

What job categories do people searching Physics Informed Machine Learning jobs in Pennsylvania look for?

The top searched job categories for Physics Informed Machine Learning jobs in Pennsylvania are:

What cities in Pennsylvania are hiring for Physics Informed Machine Learning jobs?

Cities in Pennsylvania with the most Physics Informed Machine Learning job openings:

Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Pennsylvania as of August 2026, with employment types broken down into 6% Internship, 39% Full Time, 49% Part Time, and 6% Contract. Highlights an 100% In-person job distribution.

Faculty Fellow

SIG Susquehanna

Bala Cynwyd, PA • On-site

$80 - $100/hr

Other

Posted yesterday

New

Job description

Overview

Susquehanna is launching a 12–18 month fully funded faculty fellowship. This is a unique opportunity to pursue advanced machine learning research in a fast-paced, real-world environment – collaborating with teams at the frontier of quantitative trading.

At Susquehanna, our research leverages vast and diverse datasets, applying cutting‑edge machine learning at scale to uncover actionable insights – driving data-informed decisions from predictive modeling to strategic execution.

What you'll do
  • Conduct applied machine learning research using large-scale, real-world financial datasets.
  • Develop novel modeling techniques and adapt state-of-the-art algorithms to unique challenges in quantitative finance.
  • Collaborate with researchers and engineers to translate theoretical insights into production-scale systems.
  • Contribute to the design of robust, high-performance ML infrastructure.
  • Explore research directions aligned with your interests, with flexibility in scope and duration.
  • Evaluate ideas in an industrial setting, generating insights that may inform future academic or applied work.
  • Help grow our research community by fostering collaboration and leveraging your network within the ML and academic ecosystems.
What we're looking for
  • Exceptional faculty (tenured or tenure-track) with expertise in machine learning, deep learning, LLM, statistics, computer science, physics, applied mathematics, or related fields.
  • Exceptional newly minted PhDs or postdocs developing a research agenda in machine learning, deep learning, LLM, statistics, computer science, physics, applied mathematics, or related fields.
  • A strong theoretical foundation in ML and a passion for solving practical, open-ended problems.
  • Strong programming skills (Python preferred); experience with ML frameworks like PyTorch, TensorFlow or Jax.
  • Intellectual curiosity, adaptability, and a collaborative mindset.

Note: This fellowship is ideal for faculty seeking to broaden their applied research portfolio, explore new domains, or engage in sabbatical collaborations. The faculty fellowship is also appropriate for exceptional newly minted PhD and postdocs who want to develop a research agenda (involving, but not limited to, modeling, inference, and prediction tasks in complex systems), as they prepare to transition into a faculty position. While research outputs cannot be published due to the proprietary nature of our work, we aim for each faculty fellow to publish technical research papers collaboratively with their research hosts, to showcase some of the machine learning and AI innovations that they developed while in residence at Susquehanna.

About Susquehanna

Susquehanna is a global quantitative trading firm powered by scientific rigor, curiosity, and innovation. Our culture is intellectually driven and highly collaborative, bringing together researchers, engineers, and traders to design and deploy impactful strategies in our systematic trading environment. To meet the unique challenges of global markets, Susquehanna applies machine learning and advanced quantitative research to vast datasets in order to uncover actionable insights and build effective strategies. By uniting deep market expertise with cutting‑edge technology, we excel in solving complex problems and pushing boundaries together.

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