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Physics Informed Machine Learning Jobs in Providence, RI

Akumincorp

Oncology Physicist

Brockton, MA · On-site

Ensures that all radiation oncology treatment, simulation machines and all sealed brachytherapy ... physics procedures are subjected to a risk-informed assessment designed to minimize risks to ...

Akumin

Oncology Physicist

Brockton, MA · On-site

Ensures that all radiation oncology treatment, simulation machines and all sealed brachytherapy ... physics procedures are subjected to a risk-informed assessment designed to minimize risks to ...

The Bridge at Cherry Hill

Concierge

Johnston, RI · On-site

$15.50 - $20/hr

Must demonstrate proficiency with office machines (i.e., fax, copier/scanner) Primary Job ... informed * Oversees resident check in system (i.e., educates new residents, invites participation ...

Healthtrax Fitness & Wellness

Fitness Specialist

Dartmouth, MA · On-site

$18.50 - $20.75/hr

... each machine in an effort to keep them engaged in their program and motivated to achieve their ... Where people are informed and motivated to practice good lifestyle habits including a quality ...

City Facilities Management

RHVAC Technician

Providence, RI · On-site

$25.25 - $34.50/hr

Maintain clean and safe conditions in machine and electrical rooms. * Communicate as needed with ... Whether through our world-class training programs, over 1,500 e-learning classes through City ...

City Facilities Management

RHVAC Technician

Warwick, RI · On-site

$25 - $34.25/hr

Maintain clean and safe conditions in machine and electrical rooms. * Communicate as needed with ... Whether through our world-class training programs, over 1,500 e-learning classes through City ...

City Facilities Management

RHVAC Technician

Cranston, RI · On-site

$24.50 - $33.50/hr

Maintain clean and safe conditions in machine and electrical rooms. * Communicate as needed with ... Whether through our world-class training programs, over 1,500 e-learning classes through City ...

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

As of Aug 28, 2026, the average hourly pay for physics informed machine learning in Providence, RI is $20.27, according to ZipRecruiter salary data. Most workers in this role earn between $12.64 and $25.72 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.

What are popular job titles related to Physics Informed Machine Learning jobs in Providence, RI?

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

What job categories do people searching Physics Informed Machine Learning jobs in Providence, RI look for?

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Physics Informed Machine Learning jobs near you
Infographic showing various Physics Informed Machine Learning job openings in Providence, RI as of August 2026, with employment types broken down into 6% Internship, 42% Full Time, 45% Part Time, and 7% Contract. Highlights an 100% In-person job distribution, with an average salary of $42,158 per year, or $20.3 per hour.

Postdoctoral Research Associate in Particle Astrophysics (Gaitskell)

Brown University

Providence, RI • On-site

Full-time

Re-posted 3 days ago

Brown University rating

8.1

Company rating: 8.1 out of 10

Based on 27 frontline employees who took The Breakroom Quiz

170th of 625 rated colleges and universities

Job description

Description
The Particle Astrophysics Group in the Department of Physics at Brown University will have an opening for a postdoctoral research associate starting August 1, 2025 or earlier if desired. Timing can be negotiated. The position will involve working on the LUX ZEPLIN (LZ) dark matter search experiment, on the applications of machine learning in physics data analysis, and on new photodetector development.
Details of the research programs and the members of the Brown Particle Astrophysics Group are shown at https://particleastro.brown.edu. The group is led by Prof. Rick Gaitskell and is focused on experimental searches for dark matter. Brown is a major group in the world-leading LZ 8-tonne liquid xenon TPC direct detection experiment that is currently operating underground at Sanford Lab. Detector operations and follow-up data analysis are expected to extend into 2027.
The research will include dark matter search data analysis, nuclear recoil detector calibration techniques including the use of a deuterium-deuterium accelerator source, photodetector development for next-generation experiments, and also machine learning applied in a range of physics analyses. Previous experience with noble liquid detectors, direct dark matter search experiments, photodetectors, low-background techniques, data analysis, machine learning, or Monte Carlo simulations (GEANT4) will be advantageous. We are also looking at developing future small/fast satellite missions in particle astrophysics. There are no teaching responsibilities associated with this position.
The Brown University Department has a very active program in experimental and theoretical Astrophysics, Particle Astrophysics, Cosmology, and Particle Physics.
Applications should be submitted by December 1, 2024 for full consideration, although review of applications will continue on a rolling basis until the position is filled. Any inquiries should be sent to Particleastro_postdoc@brown.edu. Submission is made online using http://apply.interfolio.com/116965.
Brown University seeks to recruit and retain a diverse workforce to maintain the excellence of the University and to offer our students richly varied disciplines, perspectives, viewpoints, and ways of knowing and learning.
Qualifications
Initial offers will be made for one year, with the potential for renewal for a further two years. The successful applicant must have completed the requirements for a Ph.D. or equivalent qualification in physics, astrophysics, computer science, or a related disciple prior to the start of the appointment.
Application Instructions
Interested candidates should submit the following application materials:
- Curriculum vitae.
- Statement of research interests. The statement of research interests should not exceed 3 pages, excluding the bibliography.
- Three letters of recommendation submitted prior to the application deadline.
Applicants should state in their cover letter how, through their research approaches and/or public engagement, they are prepared to advance Brown's strong commitment to diversity, equity, and inclusion.

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