Dr. Vasco Cadavez obtained his first degree in Animal Husbandry Engineering in 1993 and his Masters degree in Animal Production with specialisation in Feed and Nutrition in 1998 at the Portuguese University of Trás-Os-Montes e Alto Douro. After a novel research on carcass composition and quality using ultrasound, he obtained his PhD degree in 2004.

He is currently a Professor at the Animal Science Department of the Polytechnic Institute of Braganza (IPB), where he teaches the subjects of Animal Breeding, Biotechnology applied to Animal Production, Animal Production Systems and Experimental Design and Data Analysis. He is also an active Investigator at the Food Safety and Technology Group at the CIMO Mountain Research Centre, based at IPB.

Dr. Cadavez developed expertise in a wide range of mathematical and statistical modelling techniques. He has wide experience in robust, logistic and multiple regression analyses; multivariate analysis including factor analysis, cluster analysis and classification techniques, which have been applied for animal production and genetics. He has also expertise in general linear models, count data and zero-inflated models, linear and non-linear mixed models and meta-analysis, which he has applied in the fields of animal science, agriculture and food safety. Among mathematical modelling techniques, he has expertise in differential equations, dynamic modelling and optimisation.

Dr. Cadavez is very knowledgeable in computer programming, and specifically the use of the R software and SQL processing, which he teaches at undergraduate, graduate level and workshops. Some of his current research interests are the applications Bayesian analysis, meta-analysis, dynamic modelling and acceptance sampling theory in food safety and predictive microbiology. He has academically supervised 15 students, has published over 20 peer-reviewed articles, has authored/edited 2 books, has written 10 book chapters, has presented at over 50 international conferences, and has given a number of workshops of statistical methods using the R software.