PhD Residency - Machine Learning - Plane tary Health / Ecological Spec...

Employer Name Google X   Address 0000
Job Type Full Time   Address 2
Experience   City Mountain View
Education   State California
  Zip Code 94035

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Job Description

PhD Residency - Machine Learning - Planetary Health / Ecological Species-Level Abundance / Interactions Modeling
X is Alphabet’s moonshot factory. We are a diverse group of inventors and entrepreneurs who build and launch technologies that aim to improve the lives of millions, even billions, of people. Our goal: 10x impact on the world’s most intractable problems, not just 10% improvement. We approach projects that have the aspiration and riskiness of research with the speed and ambition of a startup.


Our goal at X is to make the world a radically better place. In order to do that we seek fresh unexpected perspectives, from different fields, and that’s why we’re excited about you.

Life here isn’t easy, but it’s fun. We’re trying to build things most people can’t even imagine, and we’re doing it with the hope of making a huge, positive impact on the world. You’ll be embedded into a moonshot project, where you’ll partner with team members to solve key challenges.

This isn’t your ordinary internship. You’ll be positively challenged and pushed professionally, in ways that you may have never experienced. If this excites you - keep reading.


To be placed on one of our confidential or public X projects
To get paid competitively and with Google benefits
To be part of a lively community of other Interns and Residents
To addend colloquium and discussions with team leads from across Google, DeepMind and external organizations

Due to Covid-19, internships are held remotely through 2021
Laptops and equipment will be provided
Duration: a flexible 4 mo. to 1 year program based on project team needs and your availability.

Must be enrolled in an academic program and working towards completing a PhD degree

Conceptualize, implement, and experiment with the use of novel and existing statistical and machine learning methods in quantifying and modeling species-level biodiversity.
Engage with both the academic community within ecology, entomology, and the machine learning experts within Alphabet to build solutions for solving complex problems in this space.
Work on solutions to set up remote sensing and improve data collection
Deploy, evaluate, and improve models in real settings.

Currently enrolled in a STEM Masters or PhD program such as statistics, physics, CS, applied mathematics, geophysics, or bioscience.
Completed basic coursework in statistics, calculus, linear algebra, probability, machine learning.
Experience applying and developing statistical models and machine learning (e.g. in the context of computer vision or sequential models) for specific sequential applications.
Strong interest in how ML and statistical approaches can be used to model species-level abundance, environment-species interactions, in the context of biodiversity and conservation.

Experience deploying and improving ML in large-scale production systems.
Experience in setting up remote sensing for monitoring species in the wild.
Publications in top ML / CV (e.g. NeurIPS, CVPR) or bioconservation / biodiversity venues.
Experience with probabilistic ML models for uncertainty quantification.
At X, we don't just accept difference - we celebrate it, we support it, and we thrive on it for the benefit of our employees, our products and our community. We are proud to be an equal opportunity workplace and is an affirmative action employer. We are committed to equal employment opportunity regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity or Veteran status. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements.

If you have a disability or special need that requires accommodation, please contact us at: [email protected]

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