Ph.D. position in Drug Discovery with Explainable Artificial Intelligence

The PhD position in Drug Discovery with Explainable Artificial Intelligence (XAI) represents a cutting-edge research opportunity aimed at revolutionizing the way we understand and utilize AI in the realm of pharmaceutical development. This project seeks a highly motivated candidate who is passionate about merging the domains of artificial intelligence, computational and medicinal chemistry, and pharmacology to address one of the most critical challenges in drug discovery: making the complex decision-making processes of AI models transparent, understandable, and, ultimately, more reliable.

The successful candidate will embark on a journey to develop innovative methodologies that not only illuminate the "black box" of AI algorithms used in drug design but also enhance the interpretability and trustworthiness of these models. By integrating XAI approaches with state-of-the-art drug design algorithms, the project aims to create a new generation of predictive models. These models will not only be capable of identifying promising drug candidates with higher precision but will also provide insights into why certain compounds are predicted to be effective, thereby enabling more informed decision-making throughout the drug development process.

A significant portion of the research will be dedicated to the practical application of these methodologies. This involves not only the theoretical underpinnings of XAI but also hands-on work with cheminformatics and data analysis tools. Moreover, the project aspires to contribute to the broader field of pharmaceutical sciences by publishing findings in high-impact journals and presenting at international conferences. The ultimate goal is to bridge the gap between advanced AI techniques and real-world drug development, paving the way for a new era of efficient, transparent, and informed pharmaceutical research.

This PhD position is ideal for candidates who have a background in chemistry, artificial intelligence, or computational biology/chemistry, along with a keen interest in drug discovery and a commitment to advancing the field of explainable AI in pharmaceutical research.