James J. Farrant

James J. Farrant has a diverse work experience in the field of radar systems and engineering. James J. is currently working as a Principal Radar Systems Engineer at Spartan Radar since 2021. Prior to this, they worked at Northrop Grumman for a long tenure of 15 years, starting from 2006. During their time at Northrop Grumman, James held the role of Sr. Principal Systems Engineer where they were responsible for hardware/software integration and testing of RF/Radar systems and subsystems. James J. also conducted data processing and analysis for various radar systems, developed radar software and algorithms, and worked on electro-optical systems and image processing algorithms.

Before joining Northrop Grumman, James worked as an RF Engineer Intern at Philips Medical Systems in 2005. James J. also gained experience as an Assistant Scientist at Lighting Innovations (LLC) from 2003 to 2005. Additionally, James participated in the Undergraduate Student Research Program at the National Aeronautics and Space Administration (Glenn Research Center) in 2004.

Overall, James J. Farrant has a strong background in radar systems engineering, software development, data analysis, and algorithm development, with a focus on radar signal processing, calibration, and integration. James J. has worked with various radar systems and contributed to multiple programs throughout their career.

James J. Farrant's education history is as follows:

From 2001 to 2005, they attended John Carroll University and obtained a Bachelor of Science degree in Physics.

In 2010, they enrolled at the University of Southern California and studied Computer Science until 2013. However, it is not specified whether they completed a degree in this field.

In 2020, they pursued a Nanodegree in Deep Learning with PyTorch from Udacity.

Additionally, James J. Farrant has obtained various certifications related to machine learning and TensorFlow. In 2018 and 2019, they received certifications from Coursera in subjects such as "Art and Science of Machine Learning," "Feature Engineering," "How Google does Machine Learning," "Intro to TensorFlow," "Launching into Machine Learning," "Machine Learning with TensorFlow on Google Cloud Platform Specialization," "Convolutional Neural Networks," "Deep Learning Specialization," "Sequence Models," "Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization," "Machine Learning," "Neural Networks and Deep Learning," and "Structuring Machine Learning Projects." The exact months of completion for each certification are provided, ranging from March 2018 to November 2019.