Intern in Global Discovery Chemistry, Computer Aided Drug Design

Job Description Summary Are you curious about how cutting-edge science transforms into life-changing therapies for patients across a wide range of diseases?Join our Biomedical Research (BR) organization and contribute to breakthrough innovations in Oncology, Cardiovascular, Metabolic and Renal, Immunology, and Neuroscience.The Computer-Aided Drug Discovery (CADD) group is part of the Global Discovery Chemistry (GDC) unit at Biomedical Research in Basel, Switzerland, and focuses on impacting the drug discovery pipeline through computational methods. Our work ranges from the discovery, design, and optimization of ligands binding to disease-related proteins to the prediction of their relevant physico-chemical and ADME properties.As an intern in our dynamic and multidisciplinary CADD team, you will embark on a 6-month journey to evaluate and implement cutting-edge computational technologies that have the potential to significantly impact the drug discovery process. You'll collaborate with experts across Global Discovery Chemistry, leveraging powerful new technologies to help shape the future of medicine.This is a unique opportunity to gain hands-on experience in a dynamic research environment, where your work can directly impact the development of novel therapies. You'll be part of the innovation engine of Novartis, working at the forefront of science to help deliver meaningful solutions for patients worldwide.Job Description Location: Basel, Switzerland; #LI-On-Site This internship is limited to 6 months, with the opportunity to begin in 2026 (latest June). Please note that we can only accept applicants who are eligible to work in Switzerland.  Key Responsibilities: (depending on internship project):    Curate datasets for different modalities (e.g. small molecules, peptides, macrocycles, degrader) and define evaluation criteria (e.g. for questions related to molecular property prediction). Benchmark novel descriptors for different properties and validate against Quantum Mechanics/experimental references. Implement physics-based and machine learning models to predict properties relevant for drug discovery projects. Apply and develop state-of-the-art machine-learning models for de novo design or structure prediction. Critically analyze results and integrate robust, scalable workflows into Computer-Aided Drug Design pipelines; communicate findings to the scientific community. Essential Requirements: Education: Enrolled in a Bachelor's or Master's program in chemistry, pharmacy, bioinformatics, or a related field; internship part of academic curriculum. Languages: Proficient in English Technical Skills: Strong interest in computational chemistry, machine learning, and programming (Python, RDKit). Scientific Experience: Skilled in developing computational methods, analyzing results, compiling data, and documenting scientific findings. Team & Mindset : Collaborative team player with a proactive attitude and enthusiasm for learning and innovation. Commitment to Diversity & Inclusion: Novartis is committed to building an outstanding, inclusive work environment and diverse team's representative of the patients and communities we serve. Our recruitment decisions are based on selecting the best person for the job, regardless of gender, religion, age, color, race, sexual orientation, nationality or disability. Accessibility and accommodation:   Novartis is committed to working with and providing reasonable accommodation to all individuals. If, because of a medical condition or disability, you need a reasonable accommodation for any part of the recruitment process, or in order to receive more detailed information about the essential functions of a position, please send an e-mail to , and let us know the nature of your request and your contact information. Please include the job requisition number in your message.  Application deadline:  November 13 EOB.  Please submit a cover letter that includes your motivation for the position and from when you will be available. Skills Desired