Postdoctoral Research Associate, Aerospace and Mechanical Engineering

We are looking for a very skilled Postdoctoral Research Associate to join our team with a focus on improving and developing machine learning (ML) algorithms for predicting and preventing thermal runaway (TR) in rechargeable batteries of electric vehicles. The successful candidate will integrate experimental measurements, multi-physics modeling, and advanced ML techniques to enhance predictive analytics for battery safety. This position involves close collaboration with team members and participation in a feedback loop to refine AI algorithms. A detailed knowledge of thermal-electrochemical battery testing is not required, a general understanding is sufficient.

Outstanding UA benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; UA/ASU/NAU tuition reduction for the employee and qualified family members; access to UA recreation and cultural activities; and more!

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Algorithm Development and Integration:

• Develop and enhance
  machine learning algorithms, including Support Vector Machines (SVM), Deep
  Neural Networks (DNN), and Recurrent Neural Networks (RNN), to predict thermal
  runaway (TR) events in lithium-ion batteries (LIBs).
• Integrate ML models with multi-physics modeling frameworks to improve
  predictive accuracy for battery safety.

Data Collection and Analysis:

• Collect and analyze
  temperature profiles and feature extractions from thermal images and electric
  parameter variations.
• Utilize convolutional
  neural networks (CNNs) for thermal image analysis and RNNs, including Gated Recurrent Units (GRUs)
  and Long Short-Term Memory (LSTM) networks, for numerical data analysis.
• Execute experimental protocols for data analysis and validation.

Collaboration, Reporting and Educational Contributions:

• Collaborate with team
  members on projects focusing on the thermal behavior of solid-state batteries.
• Prepare detailed
  reports and presentations summarizing research findings, methodologies, and
  progress.
• Assist in developing
  educational materials and programs that integrate traditional engineering
  techniques with advanced AI methodologies.

Publishing and Grant Writing:

• Foster collaborations
  within the Department, with other units across the University, and with team members at other
  institutes.
• Prepare and publish
  research findings in high-impact journals and present at conferences.
• Assist in writing grant
  proposals to secure funding for ongoing and future research.
• Additional duties as assigned.

Knowledge, Skills, and Abilities

• Effective collaboration skills and the ability to work in a multidisciplinary team.
• Knowledge of multi-physics modeling and its integration with ML techniques.
• Excellent communication skills, both oral and written.
• Ability to develop and integrate ML models with multi-physics simulations.
• Strong analytical skills and the ability to interpret complex data sets.
• Competence in preparing detailed technical reports and presentations.

• PhD in Mechanical Engineering, Computer Science, or a related field.
• Selected applicant must have PhD conferred upon hire.
• Track record of publications in peer reviewed journals.

• Extensive experience in machine learning algorithm development, particularly for predictive analytics and safety applications.
• Previous experience handling and analyzing complex experimental data.
• Background working with experimental data analysis and validation of ML predictions.
• Experience working with machine learning techniques, including SVM, DNN, RNN, and CNN

Cover Letter: Should clearly indicate how your skills and professional employment experience meet the Minimum and the Preferred qualifications (if applicable).