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Physics Informed Machine Learning Jobs in Hamden, CT

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How much do physics informed machine learning jobs pay per hour?

As of Aug 18, 2026, the average hourly pay for physics informed machine learning in Hamden, CT is $19.95, according to ZipRecruiter salary data. Most workers in this role earn between $12.40 and $25.34 per hour, depending on experience, location, and employer.

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.

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The top searched job categories for Physics Informed Machine Learning jobs in Hamden, CT are:

What cities near Hamden, CT are hiring for Physics Informed Machine Learning jobs?

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Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Hamden, CT as of August 2026, with employment types broken down into 6% Internship, 40% Full Time, 48% Part Time, and 6% Contract. Highlights an 100% In-person job distribution, with an average salary of $41,488 per year, or $19.9 per hour.

2026 - 2027 Assistant/Associate Professor on Term, Wu Tsai Institute and Partner Department(s) - Neu

Yale University

New Haven, CT • On-site

Full-time

Posted 12 days ago

Yale University rating

8.6

Company rating: 8.6 out of 10

Based on 64 frontline employees who took The Breakroom Quiz

67th of 618 rated colleges and universities

Job description

Description
The mission of Yale's Wu Tsai Institute (WTI) is to understand human cognition and explore human potential by sparking interdisciplinary inquiry. In this search, WTI invites exceptional candidates investigating cognition to apply for a tenure-track endowed faculty position at the Assistant or Associate Professor on Term rank, to begin July 2027 or later. There is one opening available.
We are particularly interested in researchers with an integrative approach, driving experimental or conceptual innovations to advance our understanding of cognition within its broader ecological, biological, or societal contexts. Examples of appealing research programs include, but are not limited to, how human cognition and behavior relate to homeostatic and physiological functions, unfold within real-world contexts, adapt to technological change, interact with artificial and autonomous agents, or operate within complex multi-agent systems. We welcome diverse and interdisciplinary methodologies, including neurobiological, behavioral, cognitive, ecological, and engineering.
Successful candidates will receive a tenure-track appointment in one or more departments in the Faculty of Arts and Sciences, School of Engineering and Applied Science, and/or School of Medicine. WTI is dedicated to hiring, mentoring, and advocating for colleagues who foster excellence in our community through their research, teaching, mentoring, and service. We are eager to accommodate the needs of families and welcome applications from dual-career couples to this search or a combination of Yale searches.
Background: WTI was founded in 2021 by a historic gift from Joe Tsai and Clara Wu Tsai to reveal fundamental truths about the mind through the integrated study of the brain. Based at 100 College Street in New Haven, with links to research Yale campuses, WTI rests on three pillars that coordinate its research activities: the Center for Neurodevelopment and Plasticity, the Center for Neurocognition and Behavior, and the Center for Neurocomputation and Machine Intelligence. WTI enables interdisciplinary inquiry through fellowships for co-mentored postdocs, grad students, and undergrads; shared research facilities and services with cutting-edge technologies; collaborative grants to nucleate disruptive ideas; and regular conferences, workshops, seminars, and opportunities for professional development. WTI integrates approaches across disciplines through common mathematical, statistical, and computational frameworks. WTI fosters a welcoming environment that provides equal opportunity to people of a broad range of backgrounds and perspectives. For more information, please visit the WTI website: https://wti.yale.edu.
Qualifications
Applicants must have a PhD (or equivalent) at time of hire. Current Yale scientists are not eligible for this position.
Application Instructions
The deadline to receive applications for consideration is October 15, 2026. Review of applications will begin immediately after the deadline. Applications received after the deadline will not be considered. Applicants will be informed about their application status by the end of January.
Applicants must submit the following materials for full consideration:
  • Application Form - Indicate your desired level of tenure-track appointment (Assistant Professor or Associate Professor on Term) and broad area(s) of research.
  • Research Contributions - Describe your most significant scientific discoveries and accomplishments to date. (2 pages max, references can be included in a separate page)
  • Research Plans - Describe the focus and goals of your future research program in a supportive environment like WTI; address how this vision aligns with the mission of WTI to understand cognition through interdisciplinary approaches. (2 pages max, references can be included in a separate page)
  • Teaching and Mentoring - Describe your philosophy, experience, and plans for mentoring and teaching students and how you foster productive, fair, and welcoming research and learning environments. (2 pages max)
  • Curriculum Vitae - Include your most recent CV with links to publications (or website).
  • Letters of Recommendation - You must request three letters of recommendation by the deadline.

For further information, please contact wti@yale.edu

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