Research Scientist (all genders) - Machine Learning for Small Molecules

at  Bayer

WHO YOU ARE

We are excited about your talent and passion for this position. Attracting the right talent is important to us. We would be happy to work with you to identify and create the career and development opportunities you are looking for. And if you don’t meet all the requirements, we still look forward to receiving your application. We are all constantly learning!

• You hold a PhD in (computational) chemistry, computer science, mathematics, physics, or a related field
• You are experienced in developing and applying Machine Learning approaches with a focus on chemistry and pharmaceutical use cases; a background in deep learning is a plus
• It is an advantage if you have experience with uncertainty estimation and Active Learning, including techniques such as Conformal Predictors and Bayesian models like Gaussian Processes, as well as in working with multiomics data is an advantage
• You are driven and curious to learn about experimental processes, novel mathematical approaches, and Life Science R&D at an industrial scale
• With strong analytical thinking and a scientific mindset, you are ready to challenge the status quo
• You demonstrate the ability to collaborate effectively in interdisciplinary teams and possess excellent interpersonal and communication skills
• Fluent English language skills, both written and spoken round off your profile; knowledge of German is an advantage

• You push the boundaries of state-of-the-art Machine Learning approaches in small molecules research, focusing on uncertainty estimation, navigating chemical spaces, and modeling the properties of small molecules with pharmaceutical applications, including pharmacodynamics and pharmacokinetics
• Designing and implementing Active Learning and Bayesian optimization strategies to improve pharmaceutically relevant properties of small molecules in chemical spaces of interest is part of your role
• You develop methods for uncertainty estimation to assess the applicability domains of in-silico models and deliver actionable insights to project teams
• Reducing cycle numbers in established design-make-test-analyze approaches to molecular design by augmenting design choices with data-informed compound prioritization is part of your responsibilities
• Acting as a bridging partner between our Crop Science and Pharmaceuticals divisions as part of a life science collaboration falls within your scope of work
• You collaborate closely with experimental units to translate data into tangible project impact
• Exploring uncertainty modeling and Active Learning on modalities beyond small molecules to advance treatment options across therapeutic areas is part of your duties
• Driving continuous improvements in methodologies and processes through your own expertise and experience, and by collaborating with Bayer’s scientists across organizational units, is expected in your role