The PhD course in Innovative Biomedical Technologies in Clinical Medicine is designed to carry out a training program in translational medicine. To this end, particular attention is paid not only to advanced biotechnologies (like NGS), but also to the ability to manage and analyze large amount of data (Big Data Analysis) through innovative bioinformatics and computer science tools, which take into account the overall complexity of diseases (Network Medicine). The specific educational objectives of the PhD program are the following: a) to learn epidemiological and statistical elements of genetic susceptibility in multifactorial diseases (complex traits); b) to learn the most advanced methodologies to study genome and its variability ; next-generation sequencing (NGS) techniques and related bioinformatics methodologies; design and analysis of NGS studies c) to learn the methods of studying the epigenetic modifications of the chromatin and techniques of DNA methylation analysis; d) to learn the methods for gene expression analysis e) to learn cell culture techniques, including the isolation, characterization and culturing of progenitor cells, stem cells and iPS; organoid cultures, f) to learn techniques for in vitro and ex vivo cells of innate and adaptive immunity; g) to learn the main techniques for genetic manipulation and conditioning h) to learn proteomic and metabolomic techniques for identifying disease predictors; i) learning the principles and applications of network medicine j) to learn and apply innovative imaging techniques; k) to learn the principles of pharmacology with particular reference to pharmacogenetics l) to apprehend the principles and applications of BigData m) to learn the basics of clinical trials (experimental design, statistical estimation, statistical tests and decision rules, controlled trials, randomization methods, double blind experiments, etc.), experimental data management as well as publication and interpretation of results; n) to learn the basics of in silico clinical trials (development of computer simulation models in experimental physiology, pathophysiology, pharmacokinetics and pharmacodynamics; development of computer simulation in the evaluation of a medicinal product, medical device, or a personalized medical treatment); o) to learn the principles of experimentation on animals including elements of biomedical ethics p) to understand the business and industrial aspects related to advanced biotechnology q) to design and implement predictive algorithms (machine learning) to analyze and extract useful knowledge from the large variety and dimension of available medical data, both in the form of networks (biological and social), sequences (patients’ trajectories), and unstructured data (text and images, such as in patients’ records and social data).