Intern program - Master or PhD - Data Scientist - Research

Introduction
As a Data Scientist at IBM you will help transform our clients’ data into tangible business value by analyzing information communicating outcomes and collaborating on product development. Work with Best in Class open source and visual tools along with the most flexible and scalable deployment options. Whether it’s investigating patient trends or weather patterns you will work to solve real world problems for the industries transforming how we live.

Your Role and Responsibilities
IBM Research Brazil is hiring interns to join the Industrial Technology and Science group which conducts research in accelerated discovery of novel materials for carbon capture and storage. The internship includes activities such as: – Developing and/or fine-tuning machine learning models to predict material properties from their structural description (e.g. CIF XYZ 3D atomic positions etc). – Developing and/or fine-tuning machine learning models to predict material properties from their string-based description (e.g. SMILES SMARTS SELFIES etc).
– Developing and/or fine-tuning machine learning models to predict material properties from their electronic properties (e.g. DFT electronic density grid electrostatic energy grid etc).
– Developing generative models to create new materials based their expected properties.
Todas as vagas são elegíveis para pessoas com deficiência ou reabilitadas.

Required Technical and Professional Expertise
* Ph.D. or MSc student concluding the course in or after december 2025.
* Python programming Git and Bash environment.
* Machine Learning libraries such as PyTorch and its derivatives.
* Expertise in building machine learning models including at least one of the following: Deep Learning Reinforcement Learning Active Learning Autoencoders Graph Convolutional Neural Networks Transformers and Physics-Informed Machine Learning.
* Fluency in English.
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Preferred Technical and Professional Expertise
* Advanced (pre-) training techniques including self-supervised and supervised tasks.
* ChemInformatics techniques such as material property prediction and/or generative (inverse) molecular design.
* Development of Quantitative Structure-Property Relationship (QSPR) models.