Postdoctoral Associate
Cambridge, MA · On-site
... graph neural networks. • The ability to transfer your knowledge to Engineering problems is however expected. Company : The Massachusetts Institute of Technology (MIT) is a private research ...
Postdoctoral Associate
Cambridge, MA · On-site
... graph neural networks. • The ability to transfer your knowledge to Engineering problems is however expected. Company : The Massachusetts Institute of Technology (MIT) is a private research ...
Staff Data Scientist - Sam's Fraud Prevention
Bentonville, AR · On-site
$110K - $220K/yr
... Graph Neural Networks (GNNs) and unsupervised graph embeddings to detect coordinated fraud rings and synthetic identity clusters. Spearhead development of an intelligent retail AI system combining ...
Staff Data Scientist - Sam's Fraud Prevention
Bentonville, AR · On-site
$110K - $220K/yr
... Graph Neural Networks (GNNs) and unsupervised graph embeddings to detect coordinated fraud rings and synthetic identity clusters. Spearhead development of an intelligent retail AI system combining ...
Published research in neuro-symbolic AI, knowledge graphs, graph neural networks, probabilistic reasoning, or formal methods. * Strong programming skills in Python (PyTorch, JAX, or equivalent ...
Published research in neuro-symbolic AI, knowledge graphs, graph neural networks, probabilistic reasoning, or formal methods. * Strong programming skills in Python (PyTorch, JAX, or equivalent ...
Sr. Product Manager - Tech Amazon · Bellevue, WA +1 more
Bellevue, WA · On-site
$142K - $188K/yr
Your multi-year product vision will leverage state-of-the-art techniques -- large language models, reinforcement learning, computer vision, and graph neural networks -- to fundamentally reimagine how ...
Sr. Product Manager - Tech Amazon · Bellevue, WA +1 more
Bellevue, WA · On-site
$142K - $188K/yr
Your multi-year product vision will leverage state-of-the-art techniques -- large language models, reinforcement learning, computer vision, and graph neural networks -- to fundamentally reimagine how ...
Machine Learning Engineer
New York, NY · On-site
Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic topologies to downstream spatial physics engines. * Optimize for Real-Time Execution: Optimize training and ...
Machine Learning Engineer
New York, NY · On-site
Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic topologies to downstream spatial physics engines. * Optimize for Real-Time Execution: Optimize training and ...
Experience with geometric deep learning or graph neural networks * Familiarity with CAE software or engineering simulation tools * Background in physics-informed neural networks or scientific ML
Experience with geometric deep learning or graph neural networks * Familiarity with CAE software or engineering simulation tools * Background in physics-informed neural networks or scientific ML
Senior Applied AI Engineer
San Francisco, CA · On-site
$144K - $190K/yr
Our stack runs on a 256-petaflop NVIDIA DGX cluster with NVL72 GPU infrastructure, combining Spiking Neural Networks, Graph Neural Networks, and causal inference to deliver real-time analytics that ...
Senior Applied AI Engineer
San Francisco, CA · On-site
$144K - $190K/yr
Our stack runs on a 256-petaflop NVIDIA DGX cluster with NVL72 GPU infrastructure, combining Spiking Neural Networks, Graph Neural Networks, and causal inference to deliver real-time analytics that ...
$93K - $123K/yr
Your multi-year product vision will leverage state-of-the-art techniques -- large language models, reinforcement learning, computer vision, and graph neural networks -- to fundamentally reimagine how ...
$93K - $123K/yr
Your multi-year product vision will leverage state-of-the-art techniques -- large language models, reinforcement learning, computer vision, and graph neural networks -- to fundamentally reimagine how ...
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Stay ahead of industry trends in adversarial ML, graph neural networks, sequence modeling, reinforcement learning, and MLOps practices * Communicate complex technical concepts and platform ...
Stay ahead of industry trends in adversarial ML, graph neural networks, sequence modeling, reinforcement learning, and MLOps practices * Communicate complex technical concepts and platform ...
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Machine Learning Engineer
Manhattan, NY · On-site
Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic topologies to downstream spatial physics engines. * Optimize for Real-Time Execution: Optimize training and ...
Machine Learning Engineer
Manhattan, NY · On-site
Collaborate on connecting upstream Graph Neural Networks (GNNs) or LLMs mapping schematic topologies to downstream spatial physics engines. * Optimize for Real-Time Execution: Optimize training and ...
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Senior Applied AI Engineer
San Francisco, CA · On-site
$211K - $235K/yr
Our stack runs on a 256-petaflop NVIDIA DGX cluster with NVL72 GPU infrastructure, combining Spiking Neural Networks, Graph Neural Networks, and causal inference to deliver real-time analytics that ...
Senior Applied AI Engineer
San Francisco, CA · On-site
$211K - $235K/yr
Our stack runs on a 256-petaflop NVIDIA DGX cluster with NVL72 GPU infrastructure, combining Spiking Neural Networks, Graph Neural Networks, and causal inference to deliver real-time analytics that ...
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Explore and apply AI/ML techniques, including Large Language Models (LLMs), generative AI (GenAI), and Graph Neural Networks (GNNs), to geometry, mesh, and graph-structured engineering data.
Graph Neural Networks information
What are graph neural networks?
Graph Neural Networks (GNNs) are a type of neural network specifically designed to process data structured as graphs. Unlike traditional neural networks that work with fixed-size inputs such as images or sequences, GNNs can learn from complex relationships and connections found in data like social networks, molecular structures, or transportation systems. By leveraging the graph structure, GNNs can capture both the features of individual nodes and the patterns of their connections, making them highly effective for tasks such as node classification, link prediction, and graph classification.
What are common challenges faced by professionals working with graph neural networks in industry settings?
Professionals working with Graph Neural Networks often encounter challenges such as handling large-scale graph data, ensuring efficient model training, and addressing issues related to overfitting or underfitting due to complex graph structures. Additionally, integrating GNNs into existing machine learning pipelines and interpreting model outputs for non-technical stakeholders can be demanding. Collaboration with data engineers and domain experts is typically essential to preprocess data and validate results, making strong communication skills valuable in this role.
What are the key skills and qualifications needed to thrive as a graph neural network researcher or engineer, and why are they important?
To thrive as a Graph Neural Networks (GNN) Researcher or Engineer, you need a strong background in machine learning, deep learning, mathematics, and graph theory, often supported by a relevant degree in computer science or a related field. Familiarity with technical tools such as Python, PyTorch Geometric, Deep Graph Library (DGL), and experience with large-scale data processing is essential. Strong analytical thinking, problem-solving ability, and effective communication skills help you stand out in this role. These skills are crucial for developing innovative GNN models, interpreting complex data, and collaborating with multidisciplinary teams to solve real-world problems.
What is the difference between Graph Neural Networks vs Data Scientists?
| Aspect | Graph Neural Networks | Data Scientists |
|---|---|---|
| Required Credentials | Advanced degrees in computer science, machine learning, or related fields | Bachelor's or master's in data science, statistics, or related fields |
| Work Environment | Research labs, AI development teams, tech companies | Business analytics, data analysis teams, consulting firms |
| Industry Usage | AI, machine learning, network analysis, recommendation systems | Business intelligence, predictive modeling, data visualization |
Graph Neural Networks focus on developing models that analyze graph-structured data, often requiring specialized technical skills. Data Scientists work broadly with data analysis, modeling, and visualization across various industries. While both roles involve data, Graph Neural Networks are more specialized within AI and machine learning, whereas Data Scientists have a wider scope in data-driven decision-making.
What other helpful pages are available for Graph Neural Networks?
Other pages related to Graph Neural Networks:
Postdoctoral Associate
Cambridge, MA • On-site
Massachusetts Institute of Technology
• Colleges, Universities, and Professional Schools • 10K+ employees10,000+ employees
Full-time
Re-posted just now
Massachusetts Institute Of Technology rating
8.8
Based on 39 frontline employees who took The Breakroom Quiz
Job description
Job Summary:
Massachusetts Institute of Technology is seeking a Postdoctoral Associate in Mechanical Engineering to perform cutting-edge research on machine learning models for Computer Aided Design and Engineering Design. The role involves researching, designing, and developing novel methods and algorithms for applications in the Engineering Design domain while working in a team environment.
Responsibilities:
• perform cutting-edge research on projects centered around machine learning models for Computer Aided Design and Engineering Design
• researching, designing, and developing novel methods and algorithms for applications in the Engineering Design domain
• demonstrating a commitment to deliver results
• working on research proposals
• working in a team environment
Qualifications:
Required:
• Ph.D. in computer science, mechanical engineering, applied math, or related field with significant research experience in machine-learning/AI algorithm development, specifically in deep generative models and/or graph neural networks.
• The ability to transfer your knowledge to Engineering problems is however expected.
Company:
The Massachusetts Institute of Technology (MIT) is a private research university located in Cambridge, Massachusetts. Founded in 2014, the company is headquartered in Cambridge, USA, with a team of 5001-10000 employees. The company is currently Late Stage.
Massachusetts Institute of Technology is seeking a Postdoctoral Associate in Mechanical Engineering to perform cutting-edge research on machine learning models for Computer Aided Design and Engineering Design. The role involves researching, designing, and developing novel methods and algorithms for applications in the Engineering Design domain while working in a team environment.
Responsibilities:
• perform cutting-edge research on projects centered around machine learning models for Computer Aided Design and Engineering Design
• researching, designing, and developing novel methods and algorithms for applications in the Engineering Design domain
• demonstrating a commitment to deliver results
• working on research proposals
• working in a team environment
Qualifications:
Required:
• Ph.D. in computer science, mechanical engineering, applied math, or related field with significant research experience in machine-learning/AI algorithm development, specifically in deep generative models and/or graph neural networks.
• The ability to transfer your knowledge to Engineering problems is however expected.
Company:
The Massachusetts Institute of Technology (MIT) is a private research university located in Cambridge, Massachusetts. Founded in 2014, the company is headquartered in Cambridge, USA, with a team of 5001-10000 employees. The company is currently Late Stage.
What Massachusetts Institute Of Technology employees say
Pay
Benefits
Hours and flexibility
Workplace
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About Massachusetts Institute of Technology
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Industry
Colleges, universities, and professional schools
Company size
10,000+ Employees
Headquarters location
Cambridge, MA, US
Year founded
1861