Research Intern: Molecular Modeling and Dynamics for Drug Discovery

Key Skills

Java Programming, Design, Python Programming, SQL, Git, Machine learning techniques, Deep Learning, Data Modeling, Natural Language Processing (NLP), Large scale data processing, MS Dynamics

Job Description

Over the coming decade, deep learning looks set to have a transformational impact on the natural sciences. The consequences are potentially far-reaching and could dramatically improve our ability to model and predict natural phenomena over widely varying scales of space and time. Our AI4Science team encompasses world experts in machine learning, computational chemistry, material science, quantum physics, molecular biology, fluid dynamics, software engineering, and other disciplines, who are working together to tackle some of the most pressing challenges in this field.

We are seeking intern candidates in the area of Molecular Modeling and Dynamics for drug discovery. The successful applicant is expected to contribute to a program of research at the intersection of molecular modeling, simulations, machine learning and drug discovery. This is an exceptional opportunity to drive ambitious research in a highly collaborative, diverse and global team of other researchers and engineers, to push the state of the art in deep learning for the natural sciences.

When submitting your application, include your CV with a list of publications and open source software contributions as an attachment.

Responsibilities

• Design, implement, validate and running of large-scale Molecular Modeling and Dynamics workflows applied to drug discovery. 
• Contribute to a high-impact research agenda within the context of a highly collaborative research culture. 
• Write robust research code to test new approaches or develop novel theoretical and practical insights. 
• Prepare technical papers, presentations and open-source releases of research code. 

Qualifications

• Understanding and hands-on research experience in the modeling and setup of molecular dynamics simulations for proteins and/or protein-ligand binding, the docking and parametrization of small-molecule ligands, demonstrated for example through active research in a related PhD program or equivalent research experience 
• Experience with computational drug discovery and machine learning 
• A research program demonstrated by publications in relevant top-tier journals or conferences venues (appropriate for career stage) 
• Experience in running simulations in High-Performance Computing setups
• Ability to write high quality code in Python, as well as familiarity with Git and code reviews
• Passion to see research have real-world impact on key challenges for society. 
• Demonstrable ability to work in an interdisciplinary collaborative environment, evidenced by effective communication of technical concepts to non-experts from different technical backgrounds.