Golnaz Taheri

My name is Golnaz Taheri, and I hold a Ph.D. in Computer Science. I am currently an Assistant Professor at the KTH Royal Institute of Technology, where I am also a  Data-Driven Life Science SciLifeLab and Wallenberg (DDLS) Fellow. Prior to my role at KTH, I was an Assistant Professor within the Data Science Research Group at the Department of Computer and System Sciences (DSV) at Stockholm University. My academic journey has been shaped by my deep interest in machine learning, and their transformative applications in various domains.

Research

My research is primarily focused on advancing the field of machine learning, with a special emphasis on developing novel methodologies and applying these methods to real-world problems. I am particularly interested in leveraging machine learning techniques to analyze large-scale and complex biological data. The fundamental goal of my work is to enable a deeper understanding of biological systems through data-driven insights, facilitating developmental, temporal, and spatial explorations. By integrating machine learning with biological research, I aim to uncover patterns and relationships that contribute to advancements in personalized medicine, cancer genomics, and other life science fields.

Previously, I designed and coordinated large-scale courses and supervised graduate-level research for both Master's and Ph.D. students at DSV at Stockholm University. These experiences have enabled me to contribute to the academic growth of students while fostering a collaborative learning environment, ensuring they acquire the knowledge and skills necessary to excel in computational biology.

Positions

We are looking for two highly motivated and ambitious PhD candidates to join our team and contribute to cutting-edge research in machine learning and deep learning. This is a fully funded PhD position within the Division of Computational Science and Technology at KTH. If you're interested in applying advanced machine learning techniques to expand our understanding of cancer, or in developing innovative machine-learning and deep-learning frameworks to improve drug interaction predictions, please submit your application through the KTH job portal.