Dr. Öğr. Üyesi PERİ GÜNEŞ

Biyografi

As an accomplished and seasoned professional, I bring an extensive background of success in this field, along with the qualifications that are required to make a positive impact in this role. I offer a combination of unique skills and competencies that I have developed through a lengthy and rewarding career. I have honed computer engineering and developed software skills while being proficient in critical thinking, in previous roles, I have contributed to organizational improvements, collaborative team efforts, and attainment of key business targets.  

In 2012, I started my Ph.D. in telecommunication engineering under Prof. Murat Uysal’s supervision at Ozyegin University. I was a research assistant in Labview and Zemax (Center for VLC) and a teacher assistant for Mathematics, Physics, and Matlab courses for the duration of my Ph.D.

In 2016, I joined Professor Sıddık Yarman at İstanbul University and continued my Ph.D. under his supervision. I started my study with an experimental work related to cooperative system networks on physical layer security and submitted Two papers that have been published from 

that work. I continued my work in “Machine learning and AI” which was a novel topic experimentally and numerically with Some published papers. However, the “Embedded Software Development, Image Processing, and Data Bases” courses, are my favorite areas during my academic life. At last, my project and published as a comprehensive paper in the “WPC” Journal. In January 2020 I defended my dissertation. Some soft skills that I need to have to qualify for the job are a good command of English, Strong problem-solving and analytical skills, Excellent interpersonal communication skills, research-oriented, self-motivated, and self-driven.

Developing new end-to-end wireless communication systems based on deep machine learning to provide faster wireless communication systems with lower power consumption and investigating the challenges of implementing the proposed scheme. This project expands the end-to-end idea to networks of numerous transmitters and receivers and defines a new optimization process to utilize in a deep machine learning-based massive MIMO in 6G and beyond communication systems.