At Bayer, the business problem is to solve the world's toughest challenges in agriculture, specifically aiming for a future where 'Health for all Hunger for none' is a reality. This involves improving global crops by analyzing and connecting vast amounts of data related to phenomics, genomics, environmental scenarios, and management practices. The company seeks to revolutionize critical decision-making processes in agriculture through AI and build trust in its application.
Requirements
- Educational preparation or applied experience in at least one of the following areas: Machine/Deep Learning, Bayesian Statistics, Uncertainty Quantification, Genomics, Computational Biology, Computer Science, Probability, Probabilistic modeling, Nonlinear Dynamics, Hierarchical models, Applied Mathematics, or other related quantitative discipline.
Responsibilities
- Design, build, and implement advanced AI models, including deep learning algorithms and AI digital twins, tailored to analyze complex datasets related to phenomics, genomics, and environmental factors.
- Collaborate with interdisciplinary teams across R&D to gather, preprocess, and integrate diverse datasets from agriculture, environment, and genomics, ensuring data quality and relevance.
- Conduct thorough AI analyses of industry’s most extensive global agriculture dataset to uncover insights and establish connections between phenomic traits, genomic data, and environmental conditions.
- Incorporate genomics data (e.g. high-resolution genome assemblies, k-mers, skim-seq, gene expression, etc.) into AI models to predict and optimize crop traits and resilience, enhancing overall agricultural productivity.
- Develop predictive environmental models that inform the impact of climate and weather on crops.
- Develop sophisticated predictive models that inform decision-making processes and reduce risk for crop management and improvement strategies.
- Stay updated on the latest advancements in AI, environmental modeling and genomics, applying new techniques and methodologies to refine models and enhance their accuracy.
Other
- PhD with 4+ years experience (including PhD)
- Work collaboratively with interdisciplinary scientists, IT and engineering professionals across the organization
- Foster changing ideas to produce sophisticated, intelligent and optimized predictive models
- Work on a team as an individual contributor with other Machine Learning Researchers
- Work closely with scientists, biologists, IT professionals, and engineers to align AI initiatives with organizational goals and ensure effective implementation of models.
