We are an early stage X moonshot attempting to significantly increase the sophistication of plant engineering experiments. We aim to enable a new era of agriculture - one that will not only feed the world, but also be energy, nitrogen, water, and land-use efficient, sequester carbon, and create new high-impact applications for plant-based products.
Note that early stage X projects function like startups. There will be significant ambiguity, shifting priorities, occasional sprints towards deadlines, but also a team atmosphere, dedicated team lead, and big vision.
The position is virtual, with no site presence requirement at this time, due to COVID-19 WFH restrictions. On site presence may be required if the COVID-19 restrictions are removed before the planned start date for the position.
This is a 6 month rotational position- and applications will be reviewed on a rolling-fashion.
Currently enrolled in PhD program in a quantitative discipline such as Machine Learning, Computer Science, Bioinformatics, Statistics, or Physics. You should not be planning to graduate or leave your academic program during the residency.
Experience with one or more machine learning frameworks, such as TensorFlow, JAX, or PyTorch
Fluency with Python and Linux operating systems
Ability to dedicate 100% of your time to the project for 6 months, either starting in late July to early Aug or late Dec to early Jan.
Perseverance, positive attitude, and a strong desire to improve the world through technology
Desirable qualifications:
Knowledge of molecular, systems, or plant biology
Knowledge of breeding, or the agricultural system
Experience with interpretable machine learning techniques, generative models, and Bayesian optimization
Practical experience with applying machine learning on scientific data
Track record of publications in major machine learning conferences or relevant scientific journals
In your cover letter, please include a one-page statement describing your research or relevant work experience.
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