Opportunities For Co-Op And Industry-Phd Projects

Competitive Programming Projects

Project 1: Enhancing AI-Powered Code Synthesis

• Objectives:
• • Develop new algorithms for code generation that improve upon current state-of-the-art models.
  • Implement a robust verification system to ensure the correctness of the generated code.
  • Integrate the system with Axiomatic_AI’s CDT generator and verifier.
• Expected Outcomes:
• • Enhanced code synthesis capabilities.
  • Improved accuracy and efficiency in generated code.
  • Seamless integration with Axiomatic_AI’s existing platforms.
• Requirements: Strong background in machine learning, natural language processing, and programming languages.
• References:
• • "AlphaCode: Developing Code Generation Algorithms" Research Paper
  • GitHub - AlphaCode Repository, Codex Examples

Project 2: Optimizing Code through Automated Refactoring

• Objectives:
• • Create algorithms for identifying refactoring opportunities in codebases.
  • Develop methods for automated code refactoring and optimization.
  • Test and validate the tools within real-world code repositories.
• Expected Outcomes:
• • Automated tools for code refactoring.
  • Improved code quality and performance.
  • Integration with Axiomatic_AI’s optimization suite.
• Requirements: Experience in software engineering, machine learning, and software optimization techniques.
• References:
• • AlphaProof
  • SlotFormer: Unsupervised Visual Dynamics Simulation with Object-Centric Models : Ziyi Wu, Nikita Dvornik, Klaus Greff, Thomas Kipf, and Animesh Garg arXiv preprint arXiv:2210.05861 2022

Project : AI Code Synthesis for Microelectronics Design

• Objectives:
• • Develop AI algorithms for generating Verilog/VHDL code for microelectronics designs.
  • Implement a verification system to ensure the correctness of synthesized designs.
  • Test and validate the system on real-world microelectronics projects.
• Expected Outcomes:
• • Automated code synthesis tools for microelectronics design.
  • Improved design efficiency and correctness.
  • Validation on real-world microelectronics projects.
• Requirements: Strong background in digital circuit design, Verilog/VHDL, and machine learning.
• References:
• • "Micro/Nano Circuits and Systems Design and Design Automation" Research Paper
  • GitHub - OpenROAD: Open Source EDA
  • https://arxiv.org/abs/2405.16380
  • GitHub - EDA Tools and Resources
  • https://ieeexplore.ieee.org/document/10253952

Project : AI-Driven Photonic Integrated Circuit (PIC) Design Automation

• Objectives:
• • Develop AI algorithms for synthesizing PIC designs from high-level specifications.
  • Create optimization techniques for improving PIC performance and efficiency.
  • Validate the tools with real-world PIC designs.
• Expected Outcomes:
• • AI-driven synthesis tools for PIC design.
  • Enhanced performance and efficiency of PICs.
  • Successful validation with real-world PIC projects.
• Requirements: Expertise in photonic circuit design, optimization algorithms, and machine learning.
• References:
• • https://proceedings.mlr.press/v235/chen24ad.html
  • GitHub - Photonics Simulation Tools

Digital Twins Projects

Project 1: Advanced Digital Twin Integration for AXI

• Objectives:
• • Develop methods for real-time data integration from IoT devices into digital twins.
  • Implement predictive analytics to enhance operational efficiency.
  • Validate the system in AXI-relevant industries.
• Expected Outcomes:
• • Real-time integrated digital twin systems.
  • Enhanced predictive analytics capabilities.
  • Demonstrated benefits in AXI-relevant industries.
• Requirements: Background in IoT, data analytics, and digital twin technologies.
• References:
• • "Digital Twin for Industry 4.0: Real-Time Integration and Analytics" Research Paper
  • GitHub - Azure Digital Twins
  • "Predictive Analytics in Industry 4.0 Using Digital Twins" Research Paper
  • GitHub - Industry 4.0 Solutions

Project 2: Digital Twin Framework for Engineering Systems

• Objectives:
• • Develop a scalable framework for creating digital twins of engineering systems.
  • Integrate real-time data from various engineering processes.
  • Implement analytics for design and operational optimization.
• Expected Outcomes:
• • Scalable digital twin framework for engineering systems.
  • Enhanced design and operational optimization capabilities.
  • Successful pilot deployment in AXI-relevant engineering projects.
• Requirements: Expertise in engineering design, data integration, and digital twin technologies.
• References:
• • https://nap.nationalacademies.org/catalog/26894/foundational-research-gaps-and-future-directions-for-digital-twins?utm_source=NASEM+Math+and+Statistics&utm_campaign=87b2f564c2-EMAIL_CAMPAIGN_2023_05_15_01_42_COPY_01&utm_medium=email&utm_term=0_-a0739a5cef-%5BLIST_EMAIL_ID%5D
  • NVIDIA Omniverse

Probabilistic Machine Learning Projects

Project 1: Probabilistic Models for Uncertainty Quantification in AI

• Objectives:
• • Develop new probabilistic models for uncertainty quantification.
  • Integrate these models with existing AI systems to enhance decision-making.
  • Validate the models in real-world applications.
• Expected Outcomes:
• • Improved uncertainty quantification models.
  • Enhanced reliability of AI predictions.
  • Successful integration and validation in real-world scenarios.
• Requirements: Strong background in probabilistic modeling, statistics, and machine learning.
• References:
• • https://probml.github.io/pml-book/book1.html
  • GitHub - Bayesian Deep Learning

Project 2: Integrating Factor Networks and Knowledge Graphs for Enhanced AI Reasoning

• Objectives:
• • Develop methods for integrating factor networks with knowledge graphs to represent complex relationships.
  • Apply these integrated models to enhance AI reasoning and inference capabilities.
  • Validate the effectiveness of the integrated models in real-world scenarios.
• Expected Outcomes:
• • Advanced techniques for integrating factor networks and knowledge graphs.
  • Improved AI reasoning and decision-making capabilities.
  • Validation through case studies in various domains.
• Requirements: Expertise in probabilistic graphical models, knowledge graphs, and machine learning.
• References:
• • "Knowledge Graphs: Principles and Applications" Research Paper
  • GitHub - Knowledge Graph Toolkit