Research Scientist Intern: Impact Science

Job Description

Introduction
At IBM, work is more than a job – it’s a calling: To build. To design. To code. To consult. To think along with clients and sell. To make markets. To invent. To collaborate. Not just to do something better, but to attempt things you’ve never thought possible. Are you ready to lead in this new era of technology and solve some of the world’s most challenging problems? If so, lets talk.

Your Role and Responsibilities
Are you interested in making an impact in the real world? Are you interested in developing newly explainable and responsible AI systems utilizing prior physics or earth systems-based knowledge and data-driven AI techniques and publishing them at the main AI venues as well as at companion conferences and journals? Are you interested in collaborating with software engineers to integrate your research results in IBM products? We are seeking a Research Scientist Intern interested in advanced classical machine learning, deep learning, geospatial data analytics and large scale longitudinal environmental modelling. As part of the IBM Research team, you will conduct world-class research on innovative technologies and solutions and publish in top-tier conferences and journals. You will also contribute to the commercialization of the resulting assets. Demonstrated communication skills and ability to work independently, as well as in a team, are highly desired traits. We are seeking a someone who is motivated by addressing AI problems, especially learning from less and heterogeneous spatial and temporal data and is committed to interdisciplinary collaboration. Your can-do approach to creative problem solving will be critical to the success of your team and the company.
You must be willing to work in Johannesburg, South Africa.

Required Technical and Professional Expertise
● Pursuing an MSc/Ph.D. in machine learning, artificial intelligence, computer science, applied mathematics, signal processing, statistics, or related technical fields with extensive depth in advanced mathematics, statistics, and programming
● Theoretical knowledge of and hands-on proficiency in machine learning/deep learning algorithms
● Familiarity with spatial and time-series data and methods for processing such data will be advantageous
● Familiarity with one or more machine/deep learning frameworks, such as Scikit-Learn, PyTorch, TensorFlow
● Familiarity with open-source programming software such as Python & R and coding tools such as GitHub, Jupyter Lab
● Parallel computing experience will be advantageous
● Good communication skills in English will be required

Preferred Technical and Professional Expertise