Master’s student or intern
Artificial Intelligence in Chemistry and Material Science
A master’s student or intern position is available for a candidate with a passion for driving scientific questions with a positive and problem-solving attitude and the willingness to undertake challenging tasks in a timely fashion. The candidate will join the group of Accelerated Discovery at IBM Research in Zurich that is embedded in a strong, worldwide IBM AI science community.
AI applied to Chemistry and Material Science will have profound impacts on Industries, increasing the efficiency in R&D processes and accelerating the development and adoption of sustainable solutions. Our team pushes this frontier with a culture of innovation and interdisciplinarity focusing on the creation and consumption of high quality science. All the contributions will be made world-wide available through two main channels: the Digital Chemistry platform, openly accessible world-wide to students, academics and industries, or the Generative Model Toolkit.
- Enrolled or in possession of a Master's degree in computer science, physics, chemistry, or engineering with a sincere interest in chemistry, chemical synthesis or material science.
- Prior expertise in machine learning and data models.
- High amount of creativity and outstanding problem-solving ability.
- Excellent programming skills in Python are essential.
- Experience with relevant libraries (TensorFlow/PyTorch, the python scientific stack) is necessary.
- Familiarity with version control, for example git.
- Experience with continuous integration pipelines.
- Excellent oral and written English with good presentation skills.
- Strong interpersonal skills and excellent written and verbal communication.
IBM is committed to diversity at the workplace. With us you will find an open, multicultural environment. Excellent flexible working arrangements enable all genders to strike the desired balance between their professional development and their personal lives.
How to apply
Please submit your CV including contact information for two or three references.