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Dynamical Systems Research Jobs (NOW HIRING)

NorthEastern

Postdoctoral Research Associate

Boston, MA · On-site

$60K - $85K/yr

The work will combine tools from dynamical systems, control theory, and the theory of algorithms ... Position Type Research Additional Information Northeastern University considers factors such as ...

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Dynamical Systems Research information

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How much do dynamical systems research jobs pay per year?

As of Sep 13, 2026, the average yearly pay for dynamical systems research in the United States is $68,438.00, according to ZipRecruiter salary data. Most workers in this role earn between $43,500.00 and $85,000.00 per year, depending on experience, location, and employer.

What is dynamical systems research?

Dynamical Systems Research is the scientific study of systems that evolve over time according to specific rules, often described by mathematical equations. Researchers in this field investigate how systems change, predict long-term behavior, and analyze stability and patterns, such as chaos or periodicity. Applications range from physics and biology to economics and engineering, helping to understand everything from weather patterns to population dynamics. This interdisciplinary field often uses tools from mathematics, computer science, and data analysis.

What are the key skills and qualifications needed to thrive in dynamical systems research?

To excel in Dynamical Systems Research, a strong background in mathematics, particularly differential equations and nonlinear systems, along with an advanced degree (often a PhD) is essential. Familiarity with computational tools such as MATLAB, Python, or Mathematica, and experience using simulation or modeling software, are typically required. Analytical thinking, problem-solving abilities, and clear scientific communication are crucial soft skills for success in this field. These skills are vital for advancing theoretical understanding, conducting rigorous research, and effectively sharing findings with both academic and applied audiences.

What are some common challenges faced by researchers in the field of dynamical systems, and how can they be addressed?

Researchers in dynamical systems often encounter challenges such as modeling complex, nonlinear phenomena, managing large datasets, and staying updated with rapidly evolving computational methods. Collaboration with interdisciplinary teams and regular engagement with conferences or workshops can help address these challenges. Utilizing advanced simulation tools and seeking mentorship from experienced researchers are also effective strategies for overcoming technical and conceptual obstacles.

What is the difference between Dynamical Systems Research vs Data Scientist?

AspectDynamical Systems ResearchData Scientist
Required CredentialsAdvanced degrees in mathematics, physics, or engineeringDegree in computer science, statistics, or related fields
Work EnvironmentResearch labs, academia, or R&D departmentsTech companies, finance, healthcare, or consulting firms
Industry UsageModeling complex systems, chaos theory, nonlinear dynamicsData analysis, predictive modeling, machine learning

While both roles involve analytical skills and quantitative methods, Dynamical Systems Research focuses on understanding complex systems through mathematical modeling, whereas Data Scientists analyze data to extract insights and build predictive models. The roles often overlap in research settings but differ in application and industry focus.

What are popular job titles related to Dynamical Systems Research jobs?

For Dynamical Systems Research jobs, the most frequently searched job titles are:

Infographic showing various Dynamical Systems Research job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 85% Full Time, 10% Part Time, and 3% Contract. Highlights an 85% Physical, 2% Hybrid, and 13% Remote job distribution, with an average salary of $68,438 per year, or $32.9 per hour.

Research Scientist, Dynamical Systems & AI

San Francisco, CA • On-site

Becoming
See more jobs at Becomingexternal

Full-time

Posted 25 days ago

Job description

About Becoming
Becoming is building Developmental Intelligence: AI for predicting how organisms change over time.
Most existing models work in short-horizon or static regimes. They fail when systems become long-horizon, nonlinear, and context-dependent - exactly where development, biology, and many real-world systems live.
We are building a new modeling primitive for reasoning about complex dynamics over time, starting with developmental biology and extending to other domains where prediction fundamentally breaks.
The Role
We are hiring a Research Scientist to help verify, validate, and shape a new modeling primitive designed for complex, time-evolving systems.
This role is not about incremental model improvements or benchmark chasing. Your responsibility is to rigorously evaluate whether this primitive works, where it works, where it fails, and why. You will help define its scope, limits, and evolution through careful analysis, experimentation, and comparison to existing approaches.
Your work will directly influence how the platform develops and how broadly it can be applied.
What You'll Own
  • Validation of a new AI modeling primitive for long-horizon dynamical systems
  • Design of experiments and benchmarks that test stability, generalization, and predictive fidelity over time
  • Comparative evaluation against existing modeling approaches
  • Identification of failure modes, assumptions, and edge cases
  • Clear articulation of why the model succeeds or fails in different regimes
  • Translation of findings into guidance for future architecture and system design
  • Exploration of applicability across multiple domains with complex dynamics, not just biology

Who You Are
You are someone who:
  • Operates with high agency - you identify problems, define solutions, and execute
  • Brings high energy to complex, ambiguous engineering challenges
  • Acts with high integrity - you are honest about tradeoffs, risks, and failure modes
  • Communicates directly and clearly, especially when something won't work
  • Is self-aware about your strengths and gaps, and proactively fills them
  • Thinks naturally in terms of dynamics, stability, and generalization
  • Enjoys stress-testing models to understand their limits
  • Is comfortable working on foundational problems with ambiguous answers

Requirements
Required
  • PhD or equivalent experience in applied mathematics, physics, computer science, machine learning, or a related field
  • At least 1 year of industry or applied research experience working on real modeling systems
  • Strong grounding in dynamical systems, time-series modeling, control, or sequence modeling
  • Experience evaluating models where ground truth is partial, delayed, or noisy
  • Ability to design validation strategies when standard benchmarks are insufficient
  • Comfort working with first-of-its-kind architectures and open-ended questions

Strong Signals
  • Experience with state-space models, world models, neural ODEs, diffusion over time, or hybrid approaches
  • Prior work on systems where prediction degrades over long horizons
  • Exposure to biological, physical, or other real-world dynamical systems
  • Track record of identifying why models fail - not just improving metrics

Benefits
  • Competitive salary and meaningful equity
  • Full benefits
  • High-trust, high-ownership environment
  • Rapid growth in scope and responsibility

Apply