Santa Fe, NM · On-site
$60 - $80/hr
... backward differential equations, tensor networks, and Monte Carlo approximation. The project will apply these tools to trained feedforward neural networks, restricted Boltzmann machines, and ...
Santa Fe, NM · On-site
$60 - $80/hr
... backward differential equations, tensor networks, and Monte Carlo approximation. The project will apply these tools to trained feedforward neural networks, restricted Boltzmann machines, and ...
Santa Fe, NM · On-site
$60 - $80/hr
... backward differential equations, tensor networks, and Monte Carlo approximation. The project will apply these tools to trained feedforward neural networks, restricted Boltzmann machines, and ...
Santa Fe, NM · On-site
$60 - $80/hr
... backward differential equations, tensor networks, and Monte Carlo approximation. The project will apply these tools to trained feedforward neural networks, restricted Boltzmann machines, and ...
Santa Fe, NM · On-site
... backward differential equations, tensor networks, and Monte Carlo approximation. The project will apply these tools to trained feedforward neural networks, restricted Boltzmann machines, and ...
Santa Fe, NM · On-site
... backward differential equations, tensor networks, and Monte Carlo approximation. The project will apply these tools to trained feedforward neural networks, restricted Boltzmann machines, and ...
Physics-Informed Neural Networks (PINNs) Biology-Informed Neural Networks (BINNs) / Visible Neural Networks (VNNs) Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs) Operator learning ...
Physics-Informed Neural Networks (PINNs) Biology-Informed Neural Networks (BINNs) / Visible Neural Networks (VNNs) Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs) Operator learning ...
Tulsa, OK · On-site
$125 - $150/hr
Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs) * Operator learning methods (e.g., DeepONet, Fourier Neural Operator) * Hybrid mechanistic-data-driven models * Experience with ...
New
Tulsa, OK · On-site
$125 - $150/hr
Neural Ordinary/Partial Differential Equations (Neural ODEs/PDEs) * Operator learning methods (e.g., DeepONet, Fourier Neural Operator) * Hybrid mechanistic-data-driven models * Experience with ...
New
Manhattan, NY · On-site
$150 - $200/hr
Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic ... Exceptional understanding of partial differential equations (PDEs), vector calculus, automatic ...
Manhattan, NY · On-site
$150 - $200/hr
Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic ... Exceptional understanding of partial differential equations (PDEs), vector calculus, automatic ...
New York, NY · On-site
Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic ... Exceptional understanding of partial differential equations (PDEs), vector calculus, automatic ...
New York, NY · On-site
Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic ... Exceptional understanding of partial differential equations (PDEs), vector calculus, automatic ...
San Francisco, CA · On-site
Leverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging. * Experience in Agentic-SciML is a plus. * Hands-on ...
San Francisco, CA · On-site
Leverage ML-based methods such as PiNNs and Neural Operators to solve partial differential equations arising in ultrasound simulation and imaging. * Experience in Agentic-SciML is a plus. * Hands-on ...
... neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning ...
... neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning ...
Bloomington, IN · On-site
... neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning ...
Bloomington, IN · On-site
... neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning ...
Bloomington, IN · On-site
... neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning ...
Bloomington, IN · On-site
... neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning ...
... neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning ...
... neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning ...
... neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning ...
... neural operators, physics-informed learning, inverse problems, uncertainty quantification, and rigorous methods for differential equations and scientific machine learning; geometric deep learning ...
San Francisco, CA · On-site
$125 - $150/hr
... neural ODE/SDE methods * Experience optimizing sampling efficiency (DDIM, DPM‑Solver, consistency models, etc.) * Familiarity with numerical methods for differential equations * Understanding of ...
San Francisco, CA · On-site
$125 - $150/hr
... neural ODE/SDE methods * Experience optimizing sampling efficiency (DDIM, DPM‑Solver, consistency models, etc.) * Familiarity with numerical methods for differential equations * Understanding of ...
Menlo Park, CA · On-site
$200K - $275K/yr
Hands-on experience solving partial differential equations numerically, including mesh generation ... Familiarity with physics-informed ML, surrogate modeling, or neural operators applied to simulation ...
Menlo Park, CA · On-site
$200K - $275K/yr
Hands-on experience solving partial differential equations numerically, including mesh generation ... Familiarity with physics-informed ML, surrogate modeling, or neural operators applied to simulation ...
Atlanta, GA · On-site
... learning, neural networks, and natural language processing) in a variety of environments using ... calculus/differential equations, with understanding of stochastic processes 4. Demonstrate ...
Atlanta, GA · On-site
... learning, neural networks, and natural language processing) in a variety of environments using ... calculus/differential equations, with understanding of stochastic processes 4. Demonstrate ...
Charlotte, NC · On-site
... learning, neural networks, and natural language processing) in a variety of environments using ... calculus/differential equations, with understanding of stochastic processes 4. Demonstrate ...
Charlotte, NC · On-site
... learning, neural networks, and natural language processing) in a variety of environments using ... calculus/differential equations, with understanding of stochastic processes 4. Demonstrate ...
... differential equations, probability and statistics, digital signal processing, artificial ... other neural networks, and adaptive estimation Join us in an intellectually rewarding ...
... differential equations, probability and statistics, digital signal processing, artificial ... other neural networks, and adaptive estimation Join us in an intellectually rewarding ...
... differential equations, probability and statistics, digital signal processing, artificial ... other neural networks, and adaptive estimation Join us in an intellectually rewarding ...
... differential equations, probability and statistics, digital signal processing, artificial ... other neural networks, and adaptive estimation Join us in an intellectually rewarding ...
... differential equations, probability and statistics, digital signal processing, artificial ... other neural networks, and adaptive estimation Join us in an intellectually rewarding ...
... differential equations, probability and statistics, digital signal processing, artificial ... other neural networks, and adaptive estimation Join us in an intellectually rewarding ...
| Aspect | Neural Differential Equations | Data Scientist |
|---|---|---|
| Required Credentials | Advanced degrees in mathematics, computer science, or related fields; knowledge of differential equations and machine learning | Bachelor's or master's in statistics, computer science, or related fields; strong analytical skills |
| Work Environment | Research labs, academia, or R&D departments focusing on machine learning models | Corporate, tech companies, or consulting firms analyzing data and building predictive models |
| Industry Usage | Emerging in AI research, scientific computing, and advanced modeling | Widely used across industries for data analysis, business intelligence, and decision-making |
Neural Differential Equations focus on integrating differential equations with neural networks for advanced modeling, often requiring specialized mathematical knowledge. Data Scientists analyze and interpret data to inform business decisions, typically with a broader skill set in statistics and data analysis. While both roles involve data and modeling, Neural Differential Equations are more research-oriented and technical, whereas Data Scientists apply these techniques in practical, industry settings.
Other pages related to Neural Differential Equations:
Santa Fe, NM • On-site
$60 - $80/hr
Other
Re-posted 2 days ago
The Santa Fe Institute — a private, not-for-profit research and education organization — has an opening for a two-year full-time postdoctoral fellowship. We are seeking a highly motivated scholar with expertise in physics (or in special cases in computer science), who has a desire to apply their expertise to understand the thermodynamic cost of distributed computation, from digital circuits and neural networks to human brains.
The candidate will work with PI David Wolpert on a project investigating how the network coupling the components of the distributed computer controls the tradeoff among the thermodynamic cost of running the computer, the computer's speed, its robustness against component error, and the precise computation it performs. A particular focus will be to see how the hierarchical and / or modular structure of the network controls the tradeoff among these aspects of distributed computers.
The primary tool in this investigation will be "mismatch cost" (MMC), a recently derived strengthening of the second law that applies to all physical systems — in particular those implementing computation — independent of the physical details of those systems. The central challenge, and the focus of this project, is scaling MMC calculations up to systems with very large state spaces and complicated dynamics, using techniques such as coarse-graining (in both time and space), uncertainty quantification, backward differential equations, tensor networks, and Monte Carlo approximation. The project will apply these tools to trained feedforward neural networks, restricted Boltzmann machines, and lightweight LLMs, relating their network topology—modularity, hierarchy, communication structure—to their thermodynamic cost and the difficulty of the computations they perform (including canonical problems relating computer science and thermodynamics, such as KSAT).
This position is based in-person in Santa Fe, NM. The desired start date is no later than June 1, 2027. The term of this position may be extended if appropriate and if funding allows.
Responsibilities:Collaborate with PI and other team members to advance this project through research, publication, workshop organization, etc.
Develop and apply mismatch-cost theory and large-state-space approximation techniques (e.g., coarse-graining, uncertainty quantification, backward differential equations tensor networks, Monte Carlo) to distributed computational systems
Design, train, and analyze distributed computational systems — such as feedforward neural networks, restricted Boltzmann machines, and lightweight LLMs — to relate their network topology to thermodynamic cost, robustness, and speed, and computational power.
Investigate how the "hardness" of computational problems (e.g., KSAT) relates to the thermodynamic cost of solving them.
Preferred Qualifications:Ph.D. in Physics, Computer Science, or a related field (by start date).
Background in stochastic thermodynamics, statistical physics, or theoretical computer science (computational complexity).
Familiarity with Monte Carlo methods, uncertainty quantification, backward differential equations, and/or tensor network approximations.
Strong programming skills and interest in interdisciplinary collaboration spanning physics and computer science theory.
SFI is an Equal Opportunity Employer and is committed to fostering a diverse and inclusive academic global community.
Women and members of underrepresented groups are especially encouraged to apply.
U.S. citizenship is not a requirement, however, you must be legally able to work in the US.
SFI will sponsor a J1 Visa for successful candidates.
SFI is not able to sponsor a H1B Visa for candidates.
Sourced by ZipRecruiter
Scientific research and development services
51 - 200 Employees
Santa Fe, NM, US
1984
