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Full Time Partial Differential Equations Jobs (NOW HIRING)

Vinci AI

Physics Applications - Researcher

Palo Alto, CA · On-site

$190K - $260K/yr

... partial differential equations. We are expanding this capability into the transient domain, modeling interactions, deformation and dynamics. These are promising applications where Vinci's approach ...

COMSOL

Applications Engineer: CAD & Mesh

Burlington, MA · On-site

$7/hr

COMSOL, Inc. is seeking a full-time applications engineer to join our Applications team on site in ... Knowledge of mesh-based numerical methods for solving partial differential equations (PDEs), such ...

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$55.5K

$74.9K

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How much do full time partial differential equations jobs pay per year?

As of Aug 18, 2026, the average yearly pay for full time partial differential equations in the United States is $74,877.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,500.00 and $93,500.00 per year, depending on experience, location, and employer.

What is a full time partial differential equations job?

Full Time Partial Differential Equations (PDE) jobs refer to positions where professionals work primarily with partial differential equations in a full-time capacity. These roles are typically found in academic research, applied mathematics, engineering, finance, and data science sectors. Duties may include modeling physical phenomena, developing numerical methods, performing simulations, and teaching or publishing research. Candidates usually have advanced degrees in mathematics, physics, or related fields and strong analytical skills. Employers value experience with computational tools and programming languages relevant to solving PDEs.

What are the key skills and qualifications needed to thrive as a partial differential equations specialist?

To thrive as a Partial Differential Equations (PDE) Specialist, you need an advanced degree in mathematics or applied mathematics, with a deep understanding of differential equations and mathematical analysis. Expertise in computational tools such as MATLAB, Mathematica, or Python, and familiarity with numerical methods, is typically required. Strong analytical thinking, problem-solving abilities, and clear communication help you tackle complex problems and explain findings to diverse audiences. These skills are crucial for developing accurate models and solutions in fields like engineering, physics, and finance where PDEs are foundational.

What are the typical collaborative opportunities for someone working full-time in partial differential equations?

Professionals working full-time in Partial Differential Equations often collaborate closely with interdisciplinary teams, including applied mathematicians, physicists, engineers, and computer scientists. These collaborations are crucial for developing mathematical models, analyzing complex systems, and implementing computational solutions. Teamwork typically involves regular meetings to discuss progress, share results, and troubleshoot challenges, especially when working on large-scale research projects or industry applications. Such collaborative environments foster knowledge exchange and can lead to new research directions or innovative applications of PDEs.

What is the difference between Full Time Partial Differential Equations vs Computational Mathematician?

AspectFull Time Partial Differential EquationsComputational Mathematician
Required credentialsAdvanced degrees in mathematics or applied math, specialization in PDEsMathematics, computer science, or engineering degrees, with programming skills
Work environmentResearch labs, academia, or industry R&D teams focused on modelingResearch institutions, tech companies, or academia involving algorithm development
Industry usageEngineering, physics, finance, and applied sciencesData analysis, simulation, algorithm design across various sectors

Full Time Partial Differential Equations specialists focus on solving and analyzing PDEs, often in research or applied contexts. Computational Mathematicians develop algorithms and computational methods, frequently working with PDEs but also broader mathematical problems. Both roles require strong math backgrounds, but their focus areas and work environments differ.

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Full Time Partial Differential Equations jobs near you
Infographic showing various Full Time Partial Differential Equations job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 80% Full Time, 16% Part Time, and 3% Nights. Highlights an 95% Physical, 1% Hybrid, and 4% Remote job distribution, with an average salary of $74,877 per year, or $36 per hour.

Forward Deployed Engineer, Physics & Simulation

Periodic Labs

Menlo Park, CA • On-site

$200K - $275K/yr

Full-time

Re-posted 16 days ago

Job description

About Periodic Labs
We're an AI and physical sciences company building state-of-the-art models to accelerate breakthroughs across materials, energy, and beyond. Backed by world-class investors and growing rapidly, we operate at the pace the frontier requires. Our team brings deep expertise, genuine ownership, and an insatiable drive to push the boundaries of what's scientifically possible.
About the Role
We're using AI-driven simulation to solve hard physical process optimization problems in advanced manufacturing. As a forward deployed engineer focused on physics and simulation, you will be the technical backbone of our most demanding customer engagements - spending significant time on-site, embedding directly with customer teams, and owning simulation workflows end-to-end.
You'll work with our modeling and ML teams to build and calibrate physics-based simulations, turn customer process knowledge into computational models, and drive recipe optimization with direct feedback loops to production. This is a hands-on, high-ownership role at the frontier of AI for physical science.
This role requires travel to and extended time on-site in Taiwan.
What You'll Do
  • Own the simulation workflow end-to-end for customer engagements, from model setup and calibration through optimization and results interpretation
  • Run, debug and modify physics-based simulations of complex physical processes in diverse domains, such as microfluidics, charge transport and structural deformation
  • Work on-site with customer engineering teams on-site to understand process constraints, interpret simulation results into real process improvements
  • Write tools, skills and agents to reliably drive end-to-end LLM-based simulation workflows, including experimental validation, parameter fitting and recipe optimization
  • Build and extend simulation tooling in Python - job submission, parameter sweeps, output parsing, integration
  • Feed domain insights back to the research and product teams, shaping the next version our platform
You Will Thrive in This Role If You Have
  • A strong foundation in numerical simulation of continuum systems - fluid dynamics, heat transfer, structural mechanics, electromagnetics, or similar - gained through graduate research, industry, or both
  • Hands-on experience solving partial differential equations numerically, including mesh generation, solver tuning, and debugging numerical instabilities
  • Solid Python skills for scripting and scientific computing (NumPy, SciPy, or similar)
  • A process engineer's instinct: you treat simulations as tools for answering real process questions, not just jobs to run
  • Strong communication skills and genuine comfort working directly with customer engineers Willingness to spend extended periods on-site in Taiwan
  • A self-starter mindset: you can take a technical problem from definition to deployed result without much hand-holding
Especially Strong Candidates May Also Have
  • CFD background, including tools like OpenFOAM, ANSYS Fluent, Star-CCM+, or custom solvers
  • Grad-level research experience building simulation software in domains like mechanical or chemical engineering, weather modeling, astrophysics, or materials processing
  • Experience with semiconductor or advanced packaging processes (underfill, flip-chip, wafer bonding, etc.)
  • Familiarity with physics-informed ML, surrogate modeling, or neural operators applied to simulation acceleration
  • Experience integrating simulation tools into larger software platforms or automated optimization pipelines
  • Mandarin proficiency for on-site collaboration in Taiwan
  • Lab or experimental background, with an appreciation for how simulation connects to physical data
Mechanics
Minimum education: Bachelor's degree or similar experience
Location: Menlo Park, CA (Soon: San Francisco, too) + frequent travel to Taiwan
Compensation: $200,000-$275,000 + equity
Visa sponsorship: Yes, we sponsor visas and will do everything we can to assist in this process.

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