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Physics Informed Machine Learning Jobs in Pennsylvania

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:

$14.75 - $19.75/hr

Support machine learning model development using tools and libraries such as PyTorch, Scikit-learn ... Support for data analysis and data display Students studying Math, Physics, Electrical Engineering ...

... Physics, etc.) or equivalent experience • 2+ years of experience working in data engineering, data science, software engineering, MLOps specializing in AI and Machine Learning deployment • 2+ ...

... Physics, etc.) or equivalent experience • 2+ years of experience working in data engineering, data science, software engineering, MLOps specializing in AI and Machine Learning deployment • 2+ ...

AI Solutions Architect

Pittsburgh, PA · On-site

$61.25 - $80.50/hr

Leading sales, solution design, and delivery for artificial intelligence, machine learning ... or Physics, or equivalent experience * 8+ years of experience in product sales, software ...

AI Solutions Architect

Philadelphia, PA · On-site

$63.50 - $83.75/hr

Leading sales, solution design, and delivery for artificial intelligence, machine learning ... or Physics, or equivalent experience * 8+ years of experience in product sales, software ...

Senior AI Engineer - SFL Scientific

Philadelphia, PA · On-site

$99K - $137K/yr

... Physics, etc.) or equivalent experience • 4+ years of experience working in data engineering, data science, software engineering, MLOps specializing in AI and Machine Learning deployment • 4+ ...

Senior AI Engineer - SFL Scientific

Pittsburgh, PA · On-site

$101K - $139K/yr

... Physics, etc.) or equivalent experience • 4+ years of experience working in data engineering, data science, software engineering, MLOps specializing in AI and Machine Learning deployment • 4+ ...

... machine learning. Exemplary software engineering skills are also required.Most importantly we are ... The candidate must have a PhD in Physics, Astronomy or a closely related field.This is a term ...

... physics, engineering, etc.) (Required) * 4-7 years experience building and deploying machine learning and deep learning solutions at scale; familiarity with MLOps and DevOps practices and tools ...

The successful candidate will be involved in all aspects of clinical physics services ... It also has one CyberKnife, one Radixact Tomotherapy machine, and two HDR brachytherapy ...

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Physics Informed Machine Learning information

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.

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 cities in Pennsylvania are hiring for Physics Informed Machine Learning jobs? Cities in Pennsylvania with the most Physics Informed Machine Learning job openings:
Postdoctoral Scholar - Theoretical Particle and Astroparticle Physics

Postdoctoral Scholar - Theoretical Particle and Astroparticle Physics

The Pennsylvania State University

Full-time

Posted 18 days ago


Job description

APPLICATION INSTRUCTIONS:
  • CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process. Please do not apply here, apply internally through Workday.
  • CURRENT PENN STATE STUDENT (not employed previously at the university) and seeking employment with Penn State, please login to Workday to complete the student application process. Please do not apply here, apply internally through Workday.
  • If you are NOT a current employee or student, please click "Apply" and complete the application process for external applicants.

JOB DESCRIPTION AND POSITION REQUIREMENTS:

The Eberly College of Science, Department of Physics at The Pennsylvania State University is seeking to fill a Postdoctoral Scholar position in Theoretical Particle and Astroparticle Physics to start in the fall of 2025.

The postdoc will work on topics broadly related to direct and indirect probes of physics beyond the Standard Model, using analytical and computational approaches. The successful applicant will join Dr. Carlos Blanco's research group with significant academic freedom to pursue independent research directions. The postdoc will work closely with faculty and postdocs in the physics and astronomy departments, and will have access to the resources and expertise of the Materials Research Institute (MRI) and the Institute of Computational and Data Sciences (ICDS).

Ideal candidates will have experience in astroparticle physics and BSM phenomenology. Special consideration will be given to candidates with expertise in any of the following areas:

  • Development of new direct detection methods

  • Sub-GeV dark matter searches

  • Astrophysical searches of BSM physics

  • High-energy astroparticle transport calculations

  • Materials modeling/electronic structure calculations

  • 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:

Applications must be submitted electronically and include acover letter, CV, publication list, research statement, and three letters of recommendation. Letters of recommendation should be sent by letter writers directly to Dr. Carlos Blanco at carlosblanco@psu.edu. Review of applications will begin December 15 and continue until a suitable candidate is found. This is a term appointment funded for one-year from the date of hire with possibility of renewal.

The Pennsylvania State University is committed to and accountable for advancing diversity, equity, inclusion, and sustainability in all of its forms. We embrace individual uniqueness, foster a culture of inclusion that supports both broad and specific diversity initiatives, leverage the educational and institutional benefits of diversity in society and nature, and engage all individuals to help them thrive. We value inclusion as a core strength and an essential element of our public service mission.

CAMPUS SECURITY CRIME STATISTICS:

Pursuant to the Jeanne Clery Disclosure of Campus Security Policy and Campus Crime Statistics Act and the Pennsylvania Act of 1988, Penn State publishes a combined Annual Security and Annual Fire Safety Report (ASR). The ASR includes crime statistics and institutional policies concerning campus security, such as those concerning alcohol and drug use, crime prevention, the reporting of crimes, sexual assault, and other matters. The ASR is available for review here.

Employment with the University will require successful completion of background check(s) in accordance with University policies.

EEO IS THE LAW

Penn State is an equal opportunity, affirmative action employer, and is committed to providing employment opportunities to all qualified applicants without regard to race, color, religion, age, sex, sexual orientation, gender identity, national origin, disability or protected veteran status. If you are unable to use our online application process due to an impairment or disability, please contact 814-865-1473.

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University Park, PA

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About Pennsylvania State University

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Pennsylvania State University, often referred to as Penn State, is a major, public, research-intensive university located in University Park, PA, US. This esteemed institution serves as an important player within the education industry, offering a plethora of academic programs across various disciplines. The university was founded in 1855 with the mission to provide quality education, advanced research, and service to society. Penn State holds firmly to values of integrity, respect, and excellence, fostering a diverse and inclusive community. The university is renowned for its research productivity and its high-ranking programs in areas like engineering, business, and education. One notable achievement of the institution is its designation as a "R1: Doctoral Universities – Very high research activity," demonstrating its commitment to scholarship and discovery.

Industry

Education

Company size

11 - 50 Employees

Headquarters location

University Park, PA, US

Year founded

1855

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