Careers

USA

Full-time

Job Description:

This position’s main responsibilities include Structure-Based Drug Design (SBDD), Ligand-Based Drug Design (LBDD), support of hit expansion, hit-to-lead, lead optimization and molecular docking. Proficiency in one of the benchmark drug discovery packages, e.g., Schrodinger, OpenEye, MOE or Cresset, is a must.

Key responsibilities:

• Perform structure- and ligand-based Drug Design
• Perform protein:small molecule and protein:protein docking
• Perform virtual screening of molecular databases
• Support hit-to-lead campaigns and integration of the biological data
• Perform lead optimization for further ligand refinement

Education:

• Ph.D. in relevant discipline with a minimum of 2 years of industry experience

Requirements:

• Expertise in molecular modeling techniques, protein docking methods including induced-fit docking
• Extensive knowledge in molecular or structural biology and biophysics
• Experience in the application of statistical methods to evaluate properties of studied biological systems
• Ability to learn and implement new modeling techniques
• Good programming skills
• Good communication skills and aptitude for teamwork and collaboration with experimentalists

USA

Full-time

Key Responsibilities:

• Conduct biological and chemical data analysis using IT tools.
• Design and create new software and technologies to analyze biological data.
• Support researchers to create, understand and interpret workflows and data processes.
• Manage different databases.
• Provide analytical results using visualization tools.
• Design and implement solutions within scientific applications while working with researchers to meet user requirements and desired functionality.

• Evaluate new scientific applications and systems as needed (e.g., data analysis and visualization tools).
• Introduce scientific processes and solutions to IT personnel and/or vendors in order to enable and execute solutions within existing, under-development, or new IT infrastructure/initiatives.

Qualifications:

• Ph.D. in Biological Sciences, Chemistry, Physics, Bioinformatics or related disciplines and a minimum of 2 years of experience in academia or industry.
• Ability to work independently and in a collaborative team-oriented setting.
• Candidates with prior experience in biological data analysis, e.g. proteomics are preferred.
• Demonstrated strong organizational and communication skills.

USA

Full-time

Job Description:

The Machine Learning Research team works with biomedical researchers across the drug discovery pipeline, contributing directly towards patient benefit. As a machine learning research scientist and project manager on the team, you will leverage your data science and AI skillset to transform how we discover new therapies. You will apply your skills and

innovation to:

• Apply artificial intelligence (AI) /machine learning (ML) to Small Molecule Drug Discovery workflows and data sets.
• Partner with functional area experts and other members of the team to propose and develop new methods and algorithms for complex problems.
• Conduct ML research and implement cutting-edge methods focusing on prediction, imputation, and multi-dimensional analysis of drug and drug target mechanisms of action and bioactivity.
• Write structured, tested, readable and maintainable code, and participate in code reviews to ensure code quality and distribute knowledge.
• Engage with academic groups at the forefront of the field.
• Provide professional technical support to address challenges.
• Publish high-quality and impactful scientific articles and present at conferences, business meetings and academic institutions.

Minimum Requirements:

• A graduate degree (e.g., Ph.D.) in a quantitative discipline (Computer science, Bioinformatics, Data Science, Engineering & Modeling, Applied Mathematics, Physics or related fields).
• 3+ years of relevant work experience in a research institute or biotech/pharmaceutical industry.
• Solid experience in machine/deep learning modeling and data analysis using state-of-the-art platforms and tools: Python, NumPy, sklearn, Tensorflow/Theano/Torch/Keras, etc.
• Strong software engineering skills in building complex and well-design cloud-based AI models, e.g. using AWS, GCP or Microsoft Azure