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Senior Engineer – Machine Learning

Senior Engineer – Machine Learning

CompanyAnalog Devices
LocationBoston, MA, USA, Limerick, Ireland, Burlington, MA, USA
Salary$Not Provided – $Not Provided
TypeFull-Time
DegreesPhD
Experience LevelSenior

Requirements

  • PhD degree or equivalent experience (5+ years Industrial Experience)
  • Strong background in at least one of the following areas: Algorithm Development, Machine Learning/AI, Data Analysis, Mathematical Modeling and Simulation, Signal Processing and Filtering Methods, Control Algorithms Design
  • Proficiency in MATLAB/Simulink, Python (Pandas, NumPy, SciPy, Scikit-learn, PyTorch, TensorFlow, etc.)

Responsibilities

  • Create novel algorithms specialized for applications relevant to the Automotive Electrification and Sustainable Energy Business Unit’s strategy
  • Develop software simulations and analyze performance of algorithms
  • Work with other researchers and engineers, inside and outside the AES Advanced Technology Team, to connect our work with the goals of the AES business units
  • Lead or contribute to prototyping efforts
  • Work with domain experts to define, understand, and clarify problem statements
  • Derive core mathematical expression for problems of interest
  • Build proof of concepts, and test environments to validate algorithms
  • Work with product development engineering teams to implement algorithms
  • Work with our Customers, their customers, and Research institutions to understand issues and where we can create value
  • Be a lead contributor to the technical execution of projects
  • Clearly and effectively communicate the results of analysis and action plans both verbally and in writing
  • Stay abreast of state-of-the-art algorithms, and research advances beyond the state of the art in areas relevant to AES

Preferred Qualifications

  • Experience in battery algorithms (either traditional or ML/AI based)
  • Optimization Methods
  • Digital Signal Processing
  • Battery Management Systems (BMS) design and control
  • Experience modeling electro-chemical systems