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Computational Modeling Simulation Multiphysics Jobs in New York

HSS

Postdoctoral Fellow

New York, NY · On-site

$53K - $72K/yr

Develop computer programs and scripts for computational modeling, data collection, processing, simulation, visualization, and analysis * Custom programming of cadaveric robotic systems * Imaging and ...

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Computational Modeling Simulation Multiphysics information

What is computational modeling simulation multiphysics?

Computational modeling simulation multiphysics refers to the use of computer-based models to simulate and analyze systems that involve multiple interacting physical phenomena—such as fluid dynamics, heat transfer, electromagnetics, and structural mechanics—all at once. This approach allows researchers and engineers to predict complex real-world behavior, optimize designs, and reduce the need for expensive prototypes. Multiphysics simulations are widely used in industries like aerospace, automotive, energy, and biomedical engineering, where accurate modeling of coupled physical processes is critical.

What are common challenges faced by professionals in computational modeling simulation multiphysics, and how can they be addressed?

One of the main challenges in Computational Modeling Simulation Multiphysics roles is managing the complexity of integrating multiple physical phenomena, such as thermal, structural, and fluid dynamics, into a single simulation. This often requires a deep understanding of both the underlying physics and the numerical methods used by simulation software. Collaborating closely with domain experts and maintaining clear communication within multidisciplinary teams can help address these challenges. Additionally, staying updated with advances in simulation tools and best practices through continuous learning is key to overcoming technical hurdles and ensuring accurate results.

What are the key skills and qualifications needed to thrive as a computational modeling simulation multiphysics engineer, and why are they important?

A strong background in physics, engineering, mathematics, and computational science—typically with an advanced degree—is essential for a Computational Modeling Simulation Multiphysics Engineer. Proficiency in simulation software such as ANSYS, COMSOL Multiphysics, MATLAB, and programming languages like Python or C++ is commonly required, along with familiarity with high-performance computing environments. Analytical thinking, problem-solving skills, and effective communication set standout professionals apart in this field. These capabilities enable accurate modeling of complex physical phenomena, efficient collaboration, and successful project outcomes in research and industry settings.

What is the difference between Computational Modeling Simulation Multiphysics vs Computational Engineer?

AspectComputational Modeling Simulation MultiphysicsComputational Engineer
CredentialsTypically requires degrees in engineering, physics, or related fields; certifications in simulation software are commonSimilar educational background; often holds engineering degrees and software certifications
Work EnvironmentPrimarily in R&D labs, engineering firms, or manufacturing settings focusing on complex simulationsInvolved in product development, software development, or systems design in various industries
Industry UsageUsed in aerospace, automotive, energy, and manufacturing for advanced simulationsApplied across industries for designing, analyzing, and optimizing systems and products

While both roles involve computational skills and engineering principles, Computational Modeling Simulation Multiphysics specializes in complex, multi-physics simulations, whereas Computational Engineer focuses on designing and implementing computational solutions across various engineering projects.

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Computational Modeling Simulation Multiphysics jobs near you
Infographic showing various Computational Modeling Simulation Multiphysics job openings in New York as of September 2026, with employment types broken down into 100% Full Time. Highlights an 100% In-person job distribution.

Computational Designer - Hybrid Living Textiles

New York, NY • On-site

Oxman
Specialized Design Services • 11 - 50 employees
Learn more about Oxmanexternal

$125K - $157K/yr

Full-time

Re-posted 27 days ago

Key responsibilities

  • Build pipelines that translate computational designs into machine instructions.

  • Model and predict complex behavior such as deformation, drape, stability, and structural response for simulation and design optimization.

  • Collaborate with a multidisciplinary team to translate ideas and methods across domains.

Job description

OXMAN
OXMAN is a hybrid Design and R&D company that fuses design, technology, and biology to invent multi-scale products and environments. We reject the human-centric design paradigms that have divorced us from Nature, and instead pursue a Nature-centric approach-delivering solutions by, for, and with the natural world. We design across scales for systems-level impact, treating every construct as a whole system of complex interrelations rather than an isolated object.
Role Overview
OXMAN seeks a Computational Designer to advance our work around Textile Design and Hybrid Living Materials. This person sits at the intersection of computational design, textile engineering, and synthetic biology, developing the algorithmic processes, predictive models, and automated fabrication systems through which living and engineered matter are woven together. They are fluent in code and at home in at least one of textile craft or biological reasoning-and curious enough to work across all both, treating computation as a language for mediating between digital design intent, material behavior, and biological agency.
The role spans three integrated levels: the computational design of textiles themselves; the modeling of biological behavior that plays on across those textiles; and the engineering and automation of the fabrication processes that produce them. We're looking for genuine depth in one or two, an appetite for the rest, and the instinct to translate between them.
Key Responsibilities
  • Build pipelines that translate computational designs into machine instructions.
  • Model and predict complex behavior-deformation, drape, stability, and structural response-for simulation and design optimization.
  • Develop mechanistic, dynamical, or data-driven models of biological processes (pattern formation, growth, multicellular signaling, genetic circuits).
  • Use computation to narrow large combinatorial possibility spaces.
  • Collaborate with a multidisciplinary team of engineers, biologists, and designers, translating ideas and methods across domains.
  • Communicate work clearly through meetings, presentations, and creative outlets, and maintain a shared library of processes, models, and tools.

Preferred Experience
These are the three areas the role works across. Strong candidates will bring depth in one or more and the curiosity to engage with the others.
Computational textile design
  • Develop computational processes for knit and weave design, including parametric and generative methods for structure, pattern, and form.
  • Build pipelines for the translation of computational designs into machine instructions (knit programming, jacquard instruction sets), closing the gap between geometry and fabrication.
  • Develop inverse-design methods that derive machine instructions from target geometries or performance specifications
  • Model and predict textile behavior-deformation, drape, stability, and structural response-to enable forward simulation and design optimization.

Modeling biological behavior
  • Build mechanistic and data-driven models of the biological processes that play out on and through textile substrates-genetic-circuit and gene-regulatory dynamics, and spatial pattern formation, growth, and multicellular signaling-moving fluently between deterministic (ODE) and stochastic formulations, and between first-principles models and ML as each problem warrants.
  • Calibrate and constrain these models against experimental data: parameter inference, sensitivity and identifiability analysis, and model-guided design of experiments.
  • Use computation to narrow large combinatorial possibility spaces-screening which biological and material configurations are worth realizing in vivo rather than over-specifying systems in silico.
  • Characterize and improve the stability and repeatability of living-material outcomes, building predictive models robust to biological and process variation
  • Maintain a critical, empirically grounded stance on where modeling adds value and where physical experimentation leads.

Process engineering & automation
  • Augment living-textile fabrication through design engineering and automation-programming robots, and knitting/weaving platforms
  • Design and integrate sensor systems and controllers for closed-loop monitoring and control of hybrid living-material processes.
  • Build tooling and infrastructure that make experimental processes reproducible, instrumented, and scalable across the digital-physical-biological domains.

Technical Skills
Depth in one or more of the following areas
Computational textile design:
  • Computational design tools (for example:Rhino, Grasshopper)
  • Knit/textile programming environments (KnitPaint, Stoll M1 Plus, Shima Seiki APEX); hands-on experience with CNC knitting machines (STOLL and/or Shima Seiki) and/or industrial jacquard looms
  • A strong grasp of the physics and mechanics of knitted and woven textiles, fibers, and deformation.

Biological modeling:
  • Mechanistic and/or data-driven modeling of biological or physical systems-ODE/stochastic models of genetic circuits and gene-regulatory networks, kinetic modeling, or machine learning for prediction and generative/inverse design
  • Working fluency in biological reasoning sufficient to collaborate substantively with synthetic biologists (familiarity with combinatorial assembly, multicellular behavior, or microbial systems a plus).

Process engineering & automation:
  • Robotics, CNC, sensing, and control systems-programming, integration, and closed-loop automation of fabrication processes.
Essential Qualities
  • Strong programming ability.
  • A genuinely cross-disciplinary instinct: able to translate ideas, methods, and constraints between design, engineering, and biology, and to collaborate substantively with specialists outside your own area
  • A strong "maker" mindset-rigorous, empirical, and self-critical about where modeling adds value versus where you should simply build and test
  • Portfolio demonstrating original computational work and out-of-the-box thinking
  • Excellent written and verbal communication; commitment to the team and to ethics and integrity in all work

Apply