Robert Jenkins

Data Scientist (principal Research Scientist) at The Francis Crick Institute

Robert Jenkins has a strong background in data science and research. Robert began their career at Shell as a Research Intern in 2004 before moving to Edenvale Young Associates for a research internship in 2008. In 2010, they joined Cancer Research UK (CRUK) as a Data Scientist, where they worked until 2014. Since then, they have been working at The Francis Crick Institute, starting as a Senior Research Scientist and currently holding the position of Principal Research Scientist.

Robert Jenkins completed their education in a chronological order as follows:

From 2000 to 2003, they attended the University of York and obtained a Bachelor of Science (BSc) degree in Mathematics and Statistics.

In 2003, they pursued further education at the University of York and completed a Master of Science (MSc) degree in Data Analysis, Networks, and Nonlinear Dynamics by the year 2004.

Continuing their academic journey, from 2004 to 2008, Robert Jenkins enrolled at the University of Nottingham to pursue a Doctor of Philosophy (PhD) degree. Robert'sresearch focused on the field of "Deterministic and Stochastic Modelling of Chemical Reaction Kinetics."

Additionally, Robert Jenkins obtained several certifications over the years. In 2021, they completed the certificate programs "Neural Networks and Deep Learning" and "Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization" via Coursera.

In 2022, Robert Jenkins accomplished the "Deep Learning Specialization" and "Sequence Models" certifications on Coursera. Robert also obtained two certificates from NVIDIA, namely "NVIDIA DLI Certificate – Fundamentals of Deep Learning " and "NVIDIA DLI Certificate – Fundamentals of Accelerated Computing with CUDA Python."

Finally, in 2023, Robert Jenkins completed four certifications on Coursera, specifically "Natural Language Processing in TensorFlow," "Convolutional Neural Networks in TensorFlow," "Introduction to TensorFlow for Artificial Intelligence, Machine Learning, and Deep Learning," and "Convolutional Neural Networks."

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