Daniel Contaifer Junior

Daniel Contaifer Junior

Machine Learning Scientist @ Persist AI

About Daniel Contaifer Junior

Daniel Contaifer Junior is a Machine Learning Scientist at Persist AI and a Research Assistant at Virginia Commonwealth University, where he has worked since 2015. He specializes in neural networks for pharmaceutical modeling and has a background in regulatory affairs and research.

Work at Persist AI

Daniel Contaifer Junior has been employed as a Machine Learning Scientist at Persist AI since 2022. In this role, he focuses on applying machine learning techniques to enhance the capabilities of artificial intelligence systems. His work contributes to the development of innovative solutions in the field of AI, particularly in relation to pharmaceutical applications.

Current Role at Virginia Commonwealth University

Daniel Contaifer Junior serves as a Research Assistant in the Laboratory of Pharmacometabolomics and Companion Diagnostics at Virginia Commonwealth University. He has held this position since 2015, where he engages in research activities related to pharmacometabolomics. His responsibilities include conducting experiments and analyzing data to support advancements in personalized medicine.

Previous Experience at ANVISA

Daniel Contaifer Junior worked at ANVISA as a Sanitary Surveillance Specialist from 2001 to 2004. In this capacity, he was responsible for evaluating technical dossiers for medicine registration and post-registration modifications. His role involved ensuring compliance with health regulations and contributing to public health safety.

Educational Background

Daniel Contaifer Junior holds a Bachelor of Science degree in Physiology from UERJ, which he completed from 1984 to 1988. Additionally, he earned a Bachelor of Science in Biology from Universidade do Estado do Rio de Janeiro, studying from 1983 to 1987. His educational background provides a strong foundation for his work in research and machine learning.

Research Specializations

Daniel Contaifer Junior specializes in using neural networks to model pharmaceutical formulations, which accelerates pre-clinical research. He has expertise in evaluating human coagulation parameters and conducting small animal studies that involve surgery and physiological monitoring. His experience extends to teaching and training graduate and postgraduate students, as well as physicians, in research techniques.

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