Avery Dennison Materials Scientist - Machine Learning in Mentor, Ohio
Avery Dennison Corporation (NYSE: AVY) is a global materials science company specializing in the design and manufacture of a wide variety of labeling and functional materials. The company’s products and solutions, which are used in nearly every major industry, include pressure-sensitive materials for labels and graphic applications; tapes and other bonding solutions for industrial, medical, and retail applications; tags, labels and embellishments for apparel; and radio frequency identification (RFID) solutions serving retail apparel and other markets. The company employs approximately 36,000 employees in more than 50 countries. Reported sales in 2021 were $8.4 billion. Learn more at www.averydennison.com.
At Avery Dennison, some of the great benefits we provide are:
Health & Wellness benefits starting on day 1 of employment
Paid parental leave
Flexible work arrangements
Employee Assistance Program eligibility / Health Advocate
Paid vacation and Paid holidays
Avery Dennison is looking for a highly creative, collaborative and dynamic individual to provide insightful predictive capabilities using Artificial Intelligence (AI) and Machine Learning (ML) models to accelerate the development of novel products and processes. The position will be with the Materials Science and Characterization (MSC) Group, which primarily supports R&D activities globally at Avery Dennison’s Mentor, OH location, chartered to provide fundamental mechanistic understanding and predictive insights into materials and processes, materials engineering, support and resolve technical problems from long term projects to urgent manufacturing and quality issues stemming from across Avery Dennison’s global operations. Individuals with solid foundational understanding of materials, including but not limited to polymers, gels, and adhesives, with expertise in data science and machine learning, as applied to such materials, are encouraged to apply.
● Drive fundamental understanding and insights of complex materials and phenomena using various ML frameworks and algorithms.
● Integrate physics-based modeling (Molecular Dynamics, Dissipative Particle Dynamics, multi-scale simulations, mean field and continuum approaches) with ML to enable rapid discovery of process-structure-property relationships at different length scales with limited experimental data availability.
● Identify, conceptualize, design and execute applications of ML across a wide range of disciplines including processing, materials design, quality and laminate optimization (cost-out) etc. relevant to Avery Dennison.
● Proactively interact and collaborate with other Scientists within and outside of MSC to identify and generate data required from a variety of sources experimental, simulation and literature data for the efficient development of AI models.
● Use supervised/unsupervised learning approaches to characterize the physical-chemical phase space of mixtures that will aid in the development of improved formulations.
● Design, validate and implement high quality ML algorithms and guide the development of databases and model-ready data for materials discovery with emphasis on performance, validation and accuracy.
● Interpret data, create and curate applicable databases, write technical reports, and communicate research activities and results with other divisional scientists in a high quality, professional, and timely manner.
● Translate outcomes from models to provide highly practical insights and set new avenues for exploration in multidisciplinary teams of polymer chemists, application scientists, processing, and other simulation disciplines.
● Ph.D. in Polymer/Materials Science and Engineering, Mechanical Engineering, Chemical Engineering, Physics or a related discipline with thesis research centered on the use of AI/ML in soft matter.
● Candidates with a minimum of 2 years of post-doctoral or industrial experience preferred
● Demonstrated ability in using ML in Materials Science, Chemical or Mechanical Engineering supported by a strong publication record preferred.
● Experience in algorithm designs using Python, TensorFlow, PyTorch or C/C++ with exceptional knowledge of common ML/deep learning libraries and frameworks.
● Willingness to learn and apply other modeling techniques (such as Molecular modeling, Finite Element Analysis, Computational fluid dynamics, kinetics modeling, etc.).
● Candidates with a good foundation in materials science, particularly in polymeric materials encompassing experimental and computational areas, are highly preferred.
● Excellent computer programming skills (C++, Python) and use of mathematical packages (Matlab, Mathematica, etc.) in both Windows and Linux environments.
All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, protected veteran status or other protected status. EEOE/M/F/Vet/Disabled. All your information will be kept confidential according to EEO guidelines.
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All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability, protected veteran status or other protected status. EEOE/M/F/Vet/Disabled