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
Detail-oriented and precise
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