Internship: Research Scientist (AI for Climate impacts and Risk) at IBM
- Published date: February 12, 2021
- Category: Research
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- Location: Nairobi, Nairobi
- Jop type:
- Salary: Ksh Not mentioned
- Company name: Jobs in Kenya
Job Description
Your Role and Responsibilities
IBM Research seeks one Research Scientist and/or Research Software Engineer with a strong background in at the intersection of weather, climate, and machine learning and artificial intelligence techniques to join the IBM Research Africa team and, to that matter, to the IBM Research worldwide family.
You will join IBM’s global initiative to build resilience to climate change by developing AI-enhanced and cloud-enabled regional climate predications and climate impact models to quantify climate risk. You will contribute to the development and testing of hybrid physics-AI models for key climate impacts (e.g., flood, drought, wildfire) and spatio-temporal risk models. You will collaborate with colleagues across IBM’s global research labs (Africa, UK, USA, India, Brazil, etc), and contribute to original research, aim to publish at top conferences and journals (Nature, Science, PNAS), and contribute to solutions which build climate change resilience for IBM’s clients.
The World is Our Laboratory: No matter where discovery takes place, IBM researchers push the boundaries of science, technology and business to make the world work better. IBM Research is a global community of forward-thinkers working towards a common goal: progress.
Required Technical and Professional Expertise
- Masters or Doctorate Degree (preferred) in Computer Science, Maths, Physics, or Engineering, Meteorology/Climate science or closely related field.
- Experience in numerical model development.
- Experience configuring and running weather/climate models, storm surge models or hydrological models.
- Research experience or training in climate science.
- Experience with deep learning, neural network, reinforcement learning, etc.
- Experience in ML/AI based multi-objective optimization, uncertainty quantification, explainability and knowledge graphs
Preferred Technical and Professional Expertise
None
Method of Application
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