Machine Learning Research Intern

Key Skills

Aartificial intelligence, Deep Learning, IT, Large Language Models - LLMs, Machine learning techniques, Planning and organization, Python Programming, Statistics

Job Description

The goal of this role will be to develop AI for gene expression prediction in corn. Applying various techniques, such as Large Language Modes/Transformers, will be expected. The individual will be expected to work collaboratively with interdisciplinary scientists (e.g. biologists, engineers, geneticists, machine learning scientists), IT and engineering professionals across the organization to achieve this and other critical problems and answer important questions that drive key decisions for the business.

YOUR TASKS AND RESPONSIBILITIES

The primary responsibilities of this role are to:

• Develop state of the art AI-assisted genetic discovery tools to uncover novel genetics with optimal phenotypic performance by leveraging advanced mathematical models and theoretically proven concepts of machine learning: from Large Language Models, to Bayesian optimization, and/or graph theory.
• Train and analyze AI techniques on industry s most extensive global agriculture dataset (i.e. genetic and phenotypic data) in the world, including highly controlled and real world data.
• Identify how complex genetic interactions lead to observed phenotypes in an efficient and interpretable manner and determine whether these insights lead to better design and predictions for our current and future intelligent pipelines.
• Assess feasibility and develop Artificial Intelligence/Machine Learning (AI/ML) for predicting phenotypes from high-resolution genetic inputs.
• Investigate the efficacy of gene regulatory networks to establish an interpretable representation tool for realizing new trait experiments and gene editing targets in corn.
• Be asked to lead or assist in several areas, such as: gather, curate, and quality control new data sources across Research and Development (R&D) teams.
• Work on a team as an individual contributor with other Data Scientists and builds cross functional relationships between Crop Science, Pharma, and Consumer Health divisions to collaboratively partner and effectively network within the Data Science Community.

WHO YOU ARE

Your success will be driven by your demonstration of our LIFE values, more specifically related to this position, Bayer seeks an incumbent who possesses the following:

Required Qualifications:

• Currently pursuing a Ph.D. in at least one of the following areas: Machine/Deep Learning, Bayesian Statistics, Uncertainty Quantification, Computational Biology, Computer Science, Probability, Probabilistic modeling, Nonlinear Dynamics, Hierarchical models, Applied Mathematics, or other related quantitative discipline.
• Well-versed in Python coding, data wrangling, and a solid understanding of AI/ML model basics, implementation, and tuning.
• Experience with Machine Learning areas such as supervised/unsupervised learning, language models, and/or Bayesian methods.