🚀 Internship
Internship Opportunity: AI4Science PDE Team
SLB
•
19d ago
🚀 Off-cycle
Cambridge
Rolling basis
Description
- Over the coming decade, deep learning will have a transformational impact on the natural sciences. The consequences are potentially far-reaching and could dramatically improve our ability to model and predict natural phenomena over widely varying scales of space and time. Our AI4Science team encompasses world experts in machine learning, computational chemistry, material science, quantum physics, molecular biology, fluid dynamics, software engineering, and other disciplines, who are working together to tackle some of the most pressing challenges in this field.
- We are seeking interns to join our PDE Team and contribute to a program of research at the intersection of machine learning and partial differential equations (PDEs).
Area of Responsibilities
Software Engineering
Responsibilities
- We aim to build large-scale PDE surrogates to drastically change to way physics simulations are done in science and engineering. This is an exceptional opportunity to drive ambitious research in a highly collaborative, diverse and global team of other researchers and engineers, to push the state of the art in deep learning for PDEs.
Requirements
Required Qualifications
In addition to the qualifications below, you'll need to submit a minimum of two reference letters for this position. After you submit your application, a request for letters may be sent to your list of references on your behalf. Note that reference letters cannot be requested until after you have submitted your application, and furthermore, that they might not be automatically requested for all candidates. You may wish to alert your letter writers in advance, so they will be ready to submit your letter.
- Must be currently enrolled in a PhD program in Computer Science or related STEM field.
- Interns are expected to be physically located in their manager's Microsoft worksite location for the duration of their internship.
Preferred Qualifications
A subset of the following:
- Understanding and hands-on research experience in machine learning or the natural sciences, demonstrated for example through research in a related PhD program.
- Publications in relevant top-tier journals or conference venues.
- Experience with Neural PDE modelling and/or Geometric Deep Learning.
- Experience in modelling and understanding large scale natural phenomena, such as weather or climate.
- Ability to write good quality code in Python, as well as familiarity with Git and code reviews.
- Desire to see research have real-world impact.
- Demonstrable ability to work in an interdisciplinary collaborative environment, evidenced by effective communication of technical concepts to people from different technical backgrounds
Benefits
- Industry leading healthcare
- Giving programs
- Opportunities to network and connect
- Discounts on products and services