About the Role:
We are seeking a highly motivated and curious
Computational Chemistry & AI Intern
to join our team. This stage offers a unique opportunity to gain practical, hands-on experience in applying cutting-edge AI methods to accelerate early drug discovery. The successful candidate will work closely with senior scientists to assist in evaluating drug-like molecules potential. This internship is ideal for a student finishing a Master’s degree who is passionate about the intersection of AI, computational science, and chemistry.
Responsibilities:
Support the development and application of AI/ML models for molecular design, focusing on key properties like potential activity or toxicity. Conduct literature reviews on emerging AI/ML methods, technologies, and best practices in computational chemistry. Assist in data preparation and model training using high-performance computing resources. Collaborate with the multidisciplinary research team by analyzing results and presenting findings. Actively participate in team meetings and learn about the strategic direction of research projects.
Qualifications:
Currently pursuing or recently completed a Master's degree in a relevant field such as: Computer Science, Chemistry, Mathematics, Statistics, Physics, or a related computational discipline. Coursework or project experience in AI/Machine Learning and/or computational chemistry. Familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow). Basic knowledge of Linux/Unix environments and cloud or high-performance computing (HPC) concepts is a plus. High motivation to learn and an enthusiastic, proactive approach to problem-solving. Ability to work in a collaborative and international setting. Good communication skills in spoken and written English.
Required Skills:
Currently pursuing or recently completed a Master's degree in a relevant field such as: Computer Science, Chemistry, Mathematics, Statistics, Physics, or a related computational discipline. Coursework or project experience in AI/Machine Learning and/or computational chemistry. Familiarity with at least one deep learning framework (e.g., PyTorch, TensorFlow). Basic knowledge of Linux/Unix environments and cloud or high-performance computing (HPC) concepts is a plus. High motivation to learn and an enthusiastic, proactive approach to problem-solving. Ability to work in a collaborative and international setting. Good communication skills in spoken and written English.
Preferred Skills:
Basic understanding of Graph Neural Networks (GNNs) or Transformer architectures. Familiarity with the concepts behind deep generative models (e.g., VAEs, GANs, Flows). Any project experience related to drug discovery or the prediction of ADMET properties.