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Numerical Modeling Multiscale Jobs (NOW HIRING)

... multiscale and/or multiphysics mathematical modeling, scientific computing, and numerical analysis. • Hands-on experience with deep learning, including familiarity with a range of architectures (e ...

AI Weather Scientist

San Francisco, CA · On-site

$150K - $250K/yr

Run numerical weather prediction models to generate high-resolution forecasts and training data ... Drive the development of next-generation multiscale, regional, and global weather forecasting ...

Physics Team Lead

San Francisco, CA · On-site

$210K - $250K/yr

We model what others approximate. And we build systems that change outcomes, not just prices ... Experience designing and owning multiscale or multiphysics simulation pipelines, including ...

Assistant Professor

New York, NY · On-site

$114K - $120K/yr

... 2) multiscale and multiphysics modeling of the human systems, referred to as Virtual Twin, with ... telephone number. Only electronic documents will be accepted. Please complete the online ...

Strong background in multiscale and/or multiphysics mathematical modeling, scientific computing, and numerical analysis. * Hands-on experience with deep learning, including familiarity with a range ...

Strong background in multiscale and/or multiphysics mathematical modeling, scientific computing, and numerical analysis. * Hands-on experience with deep learning, including familiarity with a range ...

Strong background in multiscale and/or multiphysics mathematical modeling, scientific computing, and numerical analysis. * Hands-on experience with deep learning, including familiarity with a range ...

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Numerical Modeling Multiscale information

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How much do numerical modeling multiscale jobs pay per hour?

As of Jun 4, 2026, the average hourly pay for numerical modeling multiscale in the United States is $23.03, according to ZipRecruiter salary data. Most workers in this role earn between $19.23 and $25.96 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Numerical Modeling Multiscale specialist, and why are they important?

To excel as a Numerical Modeling Multiscale specialist, you need a strong background in mathematics, physics, and computational science, often supported by an advanced degree in engineering or applied sciences. Expertise in simulation software (such as ANSYS, COMSOL, or MATLAB), programming languages (like Python or C++), and familiarity with high-performance computing systems are typically required. Analytical thinking, attention to detail, and effective communication skills are essential soft skills in this field. These abilities enable accurate simulations, clear interpretation of complex data, and collaborative problem-solving, which are vital for advancing research and developing innovative solutions.

What are some common challenges faced by Numerical Modeling Multiscale specialists when integrating models across different spatial and temporal scales?

Numerical Modeling Multiscale specialists often encounter challenges when coupling models that operate at different spatial and temporal resolutions. Ensuring data consistency and minimizing numerical errors at the interfaces between scales can be complex, often requiring advanced interpolation techniques and careful validation. Additionally, balancing computational efficiency with model accuracy is a frequent concern, as multiscale models can be resource-intensive. Effective collaboration with domain experts, such as physicists or engineers, is often essential to refine model parameters and interpret results accurately.

What is numerical modeling multiscale?

Numerical modeling multiscale refers to the use of computational methods to simulate and analyze systems that operate across multiple spatial or temporal scales. This approach allows researchers to study complex phenomena by linking models that describe small-scale processes, such as molecular interactions, with models of larger-scale behavior, like fluid flow or material deformation. Multiscale modeling is widely used in fields like materials science, biology, and engineering to gain insights that would be difficult or impossible to obtain through experiments alone. These models require sophisticated algorithms and significant computational resources to accurately represent interactions between different scales.

What is the difference between Numerical Modeling Multiscale vs Numerical Simulation Engineer?

AspectNumerical Modeling MultiscaleNumerical Simulation Engineer
CredentialsTypically requires advanced degrees in engineering or applied mathematicsRequires engineering or physics degrees, often with specialization in simulation
Work EnvironmentResearch labs, academia, or industry focusing on complex systemsEngineering firms, manufacturing, or software companies
Industry UsageUsed for multiscale problems across materials, fluids, and biological systemsApplied for detailed simulations of specific engineering components or processes

Numerical Modeling Multiscale focuses on developing models that bridge multiple scales in complex systems, while Numerical Simulation Engineer applies these and other simulation techniques to solve specific engineering problems. Both roles require strong analytical skills and familiarity with computational tools, but their scope and application differ.

Tenure-track faculty position at all levels in Data Science and Artificial Intelligence for Mecha...

Tenure-track faculty position at all levels in Data Science and Artificial Intelligence for Mecha...

Johns Hopkins University

Baltimore, MD • On-site

Full-time

Posted 25 days ago


Johns Hopkins Medicine rating

7.5

Company rating: 7.5 out of 10

Based on 200 frontline employees who took The Breakroom Quiz

219th of 865 rated healthcare providers


Job description

Description
The Department of Mechanical Engineering at Johns Hopkins University invites applications for tenure-track or tenured faculty positions to lead the transformation of mechanical engineering through data science (DS) and artificial intelligence (AI). We seek scholars who advance the field through foundational contributions to DS and AI, as well as apply these methods to address grand challenges in mechanical engineering. We welcome candidates whose research pushes the boundaries of what is possible when deep expertise in DS/AI intersects with core mechanical engineering domains, including fluid dynamics, mechanics and materials, dynamics and controls, robotics, biomechanics and bioengineering in human health, energy systems, space engineering, planetary health, and climate resilience.
Examples of DS/AI-centric research thrusts of interest to Mechanical Engineering include, but are not limited to:
• AI-accelerated modeling and simulation for multiphysics and multiscale problems
• Physics-informed and hybrid learning for reduced-order and surrogate models
• Uncertainty quantification, verification and validation, and risk-aware decision-making
• Learning-enabled estimation, planning, and control for autonomous and human-in-the-loop systems
• Digital twins and lifecycle analytics for complex engineered systems
• Data-centric engineering, including sensing, data assimilation, experimental design, and streaming analytics
• Generative and inverse design with autonomous design exploration
• Edge and embedded AI for real-time inference and control
• Foundations of trustworthy, robust, and interpretable AI for engineering decision-making
• Agentic and Agent-based approaches
• Computer vision
• Natural Language Processing
• DS/AI for resilience, sustainability, and planetary health
Johns Hopkins University has made a landmark investment in data science and artificial intelligence by establishing the Johns Hopkins Data Science and AI Institute(DSAI Institute). The Whiting School of Engineering at Johns Hopkins University is adding ~150 new faculty members over the next few years, constructing a new half-million-square-foot building to house the DSAI Institute, and deploying unparalleled computational infrastructure to support cutting-edge research and collaboration in this domain. This transformative initiative will establish Johns Hopkins University as one of the nation's largest and most distinguished engineering schools, featuring a world-leading AI research program. Faculty in the Department of Mechanical Engineering will play a vital role in this vision, with opportunities to engage across disciplines, access exceptional resources, and shape the future of data science and AI in engineering.
The Department of Mechanical Engineering at Johns Hopkins leads distinctive research programs. Its research spans core and emerging areas, including Mechanics and Materials, Robotics, Fluid Mechanics and Thermal Processes, Systems Modeling and Control, Mechanical Engineering in Biology and Medicine, Energy and the Environment, Micro/Nanoscale Science and Engineering, and Space Engineering. Its faculty thrives within a vibrant ecosystem of interdisciplinary collaboration that positions its faculty at the forefront of DS and AI innovation. The department operates theJohns Hopkins Turbulence Databases (JHTDB), a multi-terabyte open numerical laboratory that provides programmatic access to DNS datasets for data assimilation, model discovery, and ML benchmarking. Additionally, its faculty members are key contributors to the Artificial Intelligence for Materials Design Laboratory (AIMD-L). This automated high-throughput experimental facility generates rich microstructure-property datasets for AI-driven materials discovery and inverse design in extreme environments. Faculty also engage across an exceptional network of university institutes and centers that amplify DS and AI research impact including: the Laboratory for Computational Sensing and Robotics (LCSR) for learning-enabled perception, planning, and control; the Institute for NanoBioTechnology (INBT) for bioengineering research and discovery, the Hopkins Extreme Materials Institute (HEMI) for data-driven materials discovery and design; the Institute for Assured Autonomy(IAA) for trustworthy and verifiable autonomous systems; the Center for Environmental & Applied Fluid Mechanics(CEAFM) for physics-informed modeling of multiscale flow phenomena; the Mathematical Institute for Data Science(MINDS) for foundational theory underlying modern DS and AI methods; the Ralph O'Connor Sustainable Energy Institute (ROSEI) addressing clean energy challenges; the Malone Center for Engineering in Healthcare for medical innovation; the Johns Hopkins Institute for Planetary Health(JHIPH) at the nexus of environmental interventions and human health; and Space@Hopkins connecting civilian space research across the university. Faculty also collaborate with the Johns Hopkins Applied Physics Laboratory (APL), one of the nation's premier research and development organizations, which tackles grand challenges in national security, space exploration, and critical infrastructure. This interconnected ecosystem enables transformative research spanning from foundational algorithmic advances to high-impact applications across autonomous systems, extreme environments, sustainable energy, healthcare, space, and defense.
Qualifications
  • PhD in Mechanical Engineering or a related field by the start date.
  • A record of research excellence and a clear vision for an independent, collaborative program in DS/AI for Mechanical Engineering.
  • Commitment to high-quality teaching, mentoring, and inclusive excellence.

Application Instructions
Please submit in a single PDF or via the application portal:
  1. Cover letter,
  2. Curriculum vitae,
  3. Two-page research statement,
  4. Two-page teaching statement,
  5. Three representative publications,
  6. Names and contact information for at least three references.

Application portal: https://apply.interfolio.com/175773
Review of applications begins: Applications received by December 14th, 2025, will be fully evaluated.
Start date: July 1, 2026, or as mutually agreed upon.
Rank: Open to all ranks; appointment and salary will be commensurate with experience.

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