PhD Residency - Time-series forecasting - causal inference

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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 degreeMust be available for at least 4 consecutive months between July - Dec 2021What you should have:Currently enrolled in a Phd program in a STEM field related to Machine Learning, Computer Science, or Mathematics/StatisticsCompleted coursework in calculus, linear algebra, and probability, or their equivalent.Experience with one or more general purpose programming languages, including but not limited to: Python, Java, C/C++Experience with practical machine learning modelling, especially on time-series forecasting, analysis, and causal inference.It would be great if you also had these:Experience working in a Colab, Jupyter, or Python notebook environment a plusOpen-source projects that demonstrate relevant skills and/or publications in relevant conferences and journals (e.g. NeurIPS, ICML, ICLR, CVPR, ICCV, ECCV, ICASSP)Research experience in time-series analysis, predictive modeling and/ or multi-model coupling.Solid written and verbal communication skills.Demonstrated personal passion to apply talents towards solving big global problems.A growth mindset: you want to learn as fast as possible, value feedback and are committed to grow fast in the role