AI Research Intern – New Position

 

Recently graduated or finalising your degree in Mathematics or Physics soon?
DiRoots is a rapidly growing team of researchers, engineers, and software developers heading up design automation and Artificial Intelligence. We are currently working on two exciting projects that will be a game-changer for the AEC industry, and we need other great minds in the team.

 

Position Overview

Your job will be focused on following areas: deep learning, machine learning, control systems, simulation, optimisation, and knowledge representation applied to diverse areas such as geometry, design exploration, design automation for engineering or construction practices. This is initially a home-based position.

 

Responsibilities

  • Research, develop and document mathematical models for developers to implement
  • Collaborate with researchers and developers in the team
  • Participate in brainstorming sessions and come up with innovative ideas
  • Read relevant scientific papers and perform literature reviews
  • Evaluate existing algorithms and reproduce results on specific datasets
  • Explore, prototype and develop new AI agents and machine learning models and techniques
  • Introduce creative approaches to research topics and generates new approaches, perspectives and solutions to research topics

 

Minimum Requirements

  • Being a recent graduate (or becoming soon) in a field related to Computer Science, Mathematics or Physics
  • Excellent math skills (e.g., Linear Algebra, Probability, Statistics, Calculus)
  • Background and experience in one of the following: Artificial Intelligence, Machine Learning, Deep Learning and/or Data Science
  • Good communication skills and an awareness of how to communicate data and results effectively
  • Comfortable working in newly forming ambiguous areas where learning and adaptability are key at times, the ability to lead and rally stakeholders and team members

 

Preferable experience (but not mandatory)

  • 3D graphics/3D geometry manipulation/computational geometry
  • Knowledge Representation (semantic models, graph databases, etc.)
  • Experience with at least one of the DL platforms (TensorFlow, Keras, PyTorch, etc.)
  • Familiarity with Deep Learning techniques (e.g., Network architectures; regularisation techniques; learning techniques; loss-functions; optimisation strategies, etc.)

 

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