PhD Residency - Machine Learning - Planetary Health / Ecological Species-Level Abundance / Interactions Modeling

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Job description

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.THE FOUNDATION OF AN AMAZING JOURNEYOur 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.DURING YOUR INTERNSHIP YOU CAN EXPECT:To be placed on one of our confidential or public X projectsTo get paid competitively and with Google benefitsTo be part of a lively community of other Interns and ResidentsTo addend colloquium and discussions with team leads from across Google, DeepMind and external organizationsDETAILS:Due to Covid-19, internships are held remotely through 2021Laptops and equipment will be providedDuration: a flexible 4 mo. to 1 year program based on project team needs and your availability.REQUIREMENTS:Must be enrolled in an academic program and working towards completing a PhD degreeWHAT YOU'LL BE WORKING ON: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 collectionDeploy, evaluate, and improve models in real settings.MINIMUM QUALIFICATIONS: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.PREFERRED QUALIFICATIONS: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.