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Machine Learning Engineer, Physics-based Machine Learning

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Date: Jul 25, 2021

Location: Montreal, Quebec, CA, H4M2Z2

Company: Corning

As a Fortune 500 leader in advanced glasses and ceramics development for over a century, Corning Inc overcomes challenging engineering problems continually. The Advanced Analytics and Machine Learning Group within the Corning Technology Center, Montreal (CTCM) is a team of scientists, engineers and software developers working on broad-spectrum machine learning and data science solutions to enable some of the most exciting industrial innovations of our time.

 

WHAT YOU WILL BE DOING

 

We are looking for a talented and motivated Machine Learning and Analytics Engineer focusing on physics-based machine learning You will drive a number of Corning initiatives in data-driven physics modeling.

 

SCOPE OF THIS POSITION

 

  • Develop data-driven physical predictive models within R&D and manufacturing
  • Work on all aspects of the analytics solution development from building efficient data pipelines to implementing leading-edge inferential methods
  • Deploy scalable solutions for large datasets • Develop high-performance software solutions, primarily with the Python data-science stack, and using compiled languages such as C/C++, Fortran, C#, Java
  • Work in collaboration with project management to deliver effective and timely solutions
  • Interact regularly with research groups within Corning
  • Stay abreast of new developments in the field of physics-informed machine learning, with a constant eye on how these innovations can be applied to our problems
  • Participate in presenting new results and research innovations internally and externally
  • Cultivate and grow ties with academia
  • Mentor new hires and interns  

 

WHAT WE ARE LOOKING FOR – if you have it, let’s talk.

 

  • Strong background in numerical modeling and emerging machine learning and deep learning methods applied to numerical modeling in mechanical engineering, chemical engineering, materials science and applied physics.
  • Experience demonstrated through industrial work, academic research projects or compelling open-source project contributions.
  • Deep understanding of one or more numerical modeling domains, including but not limited to: computational fluid dynamics and heat transfer, solid mechanics, computational materials science, computational electromagnetics, molecular dynamics, agent-based modeling, cellular automata.
  • Strong interest, and preferably demonstrated background, in emerging machine learning approaches to enable and accelerate numerical simulation of physics and chemistry.
  • Strong programming background in one or more languages such as C/C++, Fortran, Python, C#, Java.
  • Excellent communication skills – both oral and written.
  • Graduate-level training in numerical simulation in mechanical / chemical / electrical / civil / materials engineering, applied math, applied physics.
  • Strong hands-on experience with the Python data science stack (Python core, NumPy, SciPy, Pandas, Matplotlib, scikit-learn and deep learning frameworks such as Tensorflow or PyTorch).
  • Experience with High Performance Computing, including General Purpose GPUs, would be a strong asset.
  • Experience in writing clean and maintainable code is critical. Working as part of a team using source management frameworks such as GIT is an asset.

 

DESIRED SOFT SKILLS

 

  • Autonomous (Self-starter)
  • Creative
  • Detail-oriented and precise
  • Team player
  • Organized

 

 

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