Rotem Rehani
About Rotem Rehani
Rotem Rehani is a Deep Learning Infrastructure Engineer at Aidoc Medical, with a strong background in computational biology and neuroscience. He has experience in software development and data analysis, having previously worked at The Hebrew University of Jerusalem in various research and teaching roles.
Work at Aidoc
Rotem Rehani has been employed at Aidoc Medical since 2018, serving as a Deep Learning Infrastructure Engineer. In this role, Rehani focuses on developing deep learning infrastructure that enhances medical imaging technology. The position is based in the Tel Aviv Area, Israel, where he contributes to the company's mission of improving healthcare through advanced AI solutions.
Education and Expertise
Rehani holds a Master’s Degree in Neurobiology and Neurosciences from The Hebrew University, which he completed from 2015 to 2017. He also earned a Bachelor’s Degree in Computer Science and Computational Biology from the same institution, graduating in 2015. His educational background supports his expertise in deep learning, computational biology, and neuroscience, enabling interdisciplinary approaches in software development.
Background in Research and Teaching
From 2014 to 2017, Rehani worked at The Hebrew University of Jerusalem in various roles, including as a Teaching Assistant and as an Experimental and Computational Researcher. His experience in these positions provided him with a solid foundation in both teaching and research methodologies. Additionally, he was involved in the Net@ project at Appleseeds Academy as a Teacher and Youth Guide from 2015 to 2016.
Previous Work Experience
Before joining Aidoc, Rehani gained experience in quality assurance as a QA Tester at OrCam in 2012 and at Mobileye from 2009 to 2010. These roles contributed to his understanding of software quality and testing processes. His diverse work history demonstrates a commitment to both technical and educational fields.
Technical Skills in Data Analysis
Rehani possesses strong technical skills in Python and MATLAB, which he utilizes for complex data analysis and algorithm development in deep learning. His proficiency in these programming languages supports his contributions to the development of deep learning infrastructure and enhances his effectiveness in the field of medical imaging technology.