Data Sciences AI/ML Summer Intern

At Johnson & Johnson, we believe health is everything. Our strength in healthcare innovation empowers us to build a world where complex diseases are prevented, treated, and cured, where treatments are smarter and less invasive, and solutions are personal. Through our expertise in Innovative Medicine and MedTech, we are uniquely positioned to innovate across the full spectrum of healthcare solutions today to deliver the breakthroughs of tomorrow, and profoundly impact health for humanity. Learn more at https://www.jnj.com/.

Johnson & Johnson Innovative Medicine is recruiting for a Summer Intern position in Drug Discovery Data Science group in In Silico Discovery (ISD) organization in La Jolla, CA. We are looking for a knowledgeable, self-motivated, creative, and multi-skilled graduate student with a strong interest in machine-learning, data science and computational chemistry. In this position, the intern will develop cutting-edged in silico design strategies that primarily uses artificial intelligence to improve the accuracy of design and optimization of bifunctional degrader's linkers.

• Build a comprehensive dataset of known public and internal linkers of bifunctional degraders encompassing their structures and biodegrading efficacy.
• Based on the examination of the dataset, the intern is expected to have a better understanding of structure‒activity relationship (SAR) of bifunctional degraders highlighting the significance that specific linker characteristics involved in their function.
• Develop machine leaning models and integrate them to our internal bifunctional degrader's drug discovery pipeline with a focus on facilitating the rapid and accurate design and optimization of novel linkers.
• Publication is also expected for this internship with the intention of disseminating our findings.

• Pursuing a PhD in a relevant field including computational chemistry, cheminformatics, data science, machine-learning, or a related field required.
• Documented coursework, training, or certification in intermediate machine-learning are required.
• Proficiency in coding in python and basic scripting in bash/shell required.
• Familiarity with python libraries such as pandas, jupyter, rdkit, scikit-learn and pytorch preferred.
• Experience with a computational chemistry toolkit (MOE/Openeye/Schrodinger, etc.) is desirable.
• Experience in targeted protein degradation is preferred but not required.
• Candidates must have authorization to work in the US.