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Fluid Dynamics Research Entry Level Jobs (NOW HIRING)

CNHES Inc.

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Thermal Research Engineer ( Junior Level )

Laconia, NH · On-site

Master's or PhD degree in Mechanical Engineering or related field Focus or coursework in heat transfer, fluid dynamics, or thermal systems required . *Laboratory experience *Graduate research ...

Worcester Polytech

Research Assistant

Worcester, MA · On-site

$21/hr

JOB TITLE Research Assistant LOCATION Worcester DEPARTMENT NAME Mechanical & Materials Engineering ... fluid dynamics and image processing. * Project 2: Applicants must hold a BS or MS in Mechanical ...

Imh

$14.50 - $18.75/hr

Computational Fluid Dynamics (CFD) and Fluid Mechanics research or practical experience The Advanced Visualization Engineering Intern supports the IMPACT Center's clinical, operational, and ...

New York University

Post Doctoral Associate

New York, NY · On-site

$65K/yr

The research group where the position resides is internationally known for its work in fluid dynamics, one of its recent activities being quantum computation of fluid dynamics (QCFD). Expectations ...

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Fluid Dynamics Research Entry Level information

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How much do fluid dynamics research entry level jobs pay per year?

As of Sep 5, 2026, the average yearly pay for fluid dynamics research entry level in the United States is $95,315.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,000.00 and $109,000.00 per year, depending on experience, location, and employer.

What are the most commonly searched types of Fluid Dynamics Research jobs?

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Fluid Dynamics Research Entry Level jobs near you
Infographic showing various Fluid Dynamics Research Entry Level job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 88% Full Time, 10% Part Time, and 1% Contract. Highlights an 78% Physical, 4% Hybrid, and 18% Remote job distribution, with an average salary of $95,315 per year, or $45.8 per hour.

Member of Technical Staff -- Research, Physics

Causal Labs

San Francisco, CA • On-site

$180 - $240/hr

Other

Re-posted 15 days ago

Job description

Our mission is general causal intelligence; AI that is capable of (1) predicting the future and (2) identifying the actions to alter it.

To achieve this breakthrough, we are building a Large Physics foundation Model (LPM) because physical systems, unlike text or images, are governed by verifiable cause and effect. We believe that scaling on physics will enable an understanding of causality required to predict and control physical systems, starting with weather.

Our founding team has built and deployed AI against the physical world in robotics, drug discovery, and particle physics at institutions like DeepMind, Waymo, Cruise, Insitro, Nabla Bio, and CERN.

We look for domain experts who are excited to tackle unsolved problems. Our thesis is that scaling on physics yields a model capable of understanding the causal structure to predict and alter the future. Your mission is to ensure the model evolves towards this thesis: grounded in physical law, evaluated against it, and ready to generalize across domains.

Responsibilities

  • Bring physical principles to bear on the model — assessing consistency with conservation laws and physical constraints, and where physics-informed inductive biases help or hinder

  • Develop evaluations that test whether the model's behavior is physically coherent, not just statistically accurate

  • Advise on the physics of the systems we model, from fluid dynamics to thermodynamics, and their numerical treatment

  • Investigate where the LPM generalizes across physical domains and where it breaks down

  • Partner with model, evaluation, and interpretability teams to connect physical understanding to research direction

What we're looking for

We value a relentless approach to problem-solving, rapid execution, and the ability to quickly learn in unfamiliar domains.

  • Deep expertise in physics — fluid dynamics, thermodynamics, computational physics, or a closely related field (typically a PhD or equivalent research experience)

  • Familiarity with numerical simulation of physical systems (e.g. CFD) and its trade-offs

  • Interest in where machine learning and physical modeling meet

  • Ability to collaborate closely with ML researchers and translate physical principles into technical requirements

  • A rigorous, evidence-driven approach to evaluating model quality

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