Overview
Skills
Job Details
Job Title: Senior AI Researcher Agriculture & Life Sciences
Location: Remote
About the Role:
We are seeking a Senior AI Researcher to drive innovative AI initiatives in agriculture and life sciences. The ideal candidate will have deep expertise in machine learning, genomics, and computational biology, and will be passionate about leveraging AI to enhance crop traits, resilience, and overall agricultural productivity.
Key Responsibilities:
AI Model Training: Assemble data and fine-tune large language models and other AI models with a focus on biology and life sciences.
Data Integration: Collaborate with interdisciplinary teams to gather, preprocess, and integrate diverse datasets from agriculture, environment, and genomics, ensuring data quality and relevance.
Protein Design: Use fine-tuned models to generate novel designs of native plant genes to improve target phenotypes.
Genomics Modeling: Incorporate genomics data (e.g., genome assemblies, k-mers, skim-seq, gene expression) into AI models to predict performance in various populations and optimize crop traits.
Collaboration: Work closely with scientists, biologists, IT professionals, and engineers to align AI initiatives with organizational goals and ensure effective implementation.
Trust Building: Communicate the benefits and limitations of AI in agriculture to stakeholders, fostering trust and transparency.
Continuous Improvement: Stay updated on advancements in AI and genomics, applying new techniques to refine models and enhance accuracy.
Documentation and Reporting: Prepare detailed documentation of methodologies, findings, and model performance; present results to both technical and non-technical audiences.
Team Contribution: Participate actively in team meetings and collaborative projects, sharing knowledge and insights with fellow researchers.
Required Qualifications:
PhD with 5+ years of experience (including PhD) in one or more 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 related quantitative disciplines.
Proven experience in applying AI/ML methods to biological or life sciences data is highly desirable.
Strong collaboration, communication, and documentation skills.