Conduct research using Deep Reinforcement Learning techniques within the plant breeding context.
Explore novel algorithms and methodologies to address AI challenges in plant breeding technologies.
Contribute to digital twin environments providing model-simulated outcomes determining reward within RL training loops.
Collaborate with cross-functional teams to integrate AI solutions into seed product development.
Design, implement, and optimize analytical pipelines for performance and efficiency.
Write software following best practices for producing readable, repeatable, and reusable code.
Guide AI models from research and development to production and deployment where necessary.
Collaborate with Breeders, Breeder Analysts, and Data Scientists to develop state-of-the-art AI and ML models to help make decisions to advance next-generation seed products.
Engage with external collaborators when needed, and actively lead or contribute towards scientific manuscripts describing the scientific work being performed.
Requirements & Skills:
Ph.D. in Data Science, Computer Science, Machine Learning, or Artificial Intelligence
Alternatively, a Ph.D. in, Computer Engineering, Electrical Engineering, Computational Biology, Physics, Mathematics or related field or related scientific discipline with 2+ years of demonstrated experience specifically in Artificial Intelligence, Machine Learning, Reinforcement Learning, and Generative AI.
Deep theoretical and applied understanding of reinforcement learning (RL) and the application of RL with deep neural networks
Strong foundation in deep learning methodologies, attention and transformer-based architectures, self-supervised learning, policy-gradient algorithms, Monte Carlo techniques, mathematics, and probability.
Strong programming skills in Python and C/C++ programming with the ability to quickly create prototype solutions on Unix / Linux / embedded platforms.
Experience working with open-source libraries and toolkits Numpy, Jax, Torch, Scikit-Learn, TensorFlow, etc.
Interest in learning new technologies, programming techniques, languages, and operating systems.
Excellent interpersonal skills and a can-do attitude with the ability to thrive in a fast-paced dynamic environment. Experience in research, life sciences, or in data science is a plus.
Excellent analytical and problem-solving skills with the ability to work as part of a global team and, at times, independently while appropriately prioritizing tasks.
Strong verbal and written communication skills in English are required.
