Postdoctoral Position in Materials Science with Focus on Automated Materials Exploration

Postdoctoral Position in Materials Science with Focus on Automated Materials Exploration

Uppsala University

Uppsala, Sweden

Project description

This project focuses on developing autonomous workflows in a self-driving laboratory (SDL) for the exploration of new energy materials. The SDL integrates sputtering, reactive thermal processing, and hyperspectral imaging to fabricate and characterize combinatorial thin-film libraries across a broad chemical and thermodynamic space. The project’s core challenge is guiding exploration within the experimental space, to uncover synthesis–property relationships for targeted materials. This will involve combining materials science expertise with machine learning approaches, both existing and newly developed, to design and execute multi-stage experimental workflows with distinct objectives. A central outcome will be the demonstration of the first PVD-based SDL governed by knowledge-driven (not only data-driven) exploration strategies. The project will involve working closely with a team including PhD students in machine learning, automation and materials science, an environment of thin film science and engineering experts, and with collaborations in AI, data science and SDL development.

Duties

You are responsible for selecting and applying workflow control software and integrating this with the SDL operations, involving interfacing with Python codes developed by others in the group (for machine learning, analysis etc.). Based on this, you are able to creatively develop, execute and test experimental sequences and evaluate their performance in reaching various scientific objectives in reaching various scientific objectives (such as choosing most efficient synthesis recipes, or rapidly fitting process models), including studying their reproducibility and robustness over time. This will be supplemented by external materials characterization (e.g. compositional and structural analysis). Ultimately, optimized and robust workflows for generating new knowledge about thin film materials will be demonstrated and described in publications. You also support the evolving SDL development and may be involved in related hardware design, programming and robotics tasks. Robust data management following FAIR principles is integral, and thus an additional task is to help integrate the developed workflows with the NOMAD database, supported by collaboration in the FAIRMAT project.

In addition to research, some participation in supervision of doctoral students, master's and project students as well as communication of research results through scientific articles in well-established scientific journals and presentations at international conferences are also included as important aspects of the employment. Some participation in undergraduate teaching may also be included. You are also expected to contribute to applications for external research funds

Requirements

To qualify for an employment on a postdoctoral position you must have a doctoral degree or a foreign degree equivalent to a PhD degree. The degree needs to be obtained by the time of the decision of employment. Those who have obtained a PhD degree three years prior to the application deadline are prioritized for the employment. The starting point of the three-year frame period is the application deadline. Due to special circumstances, the degree may have been obtained earlier. The three-year period can be extended due to circumstances such as sick leave, parental leave, duties in labor unions, etc.

For this position, you must have:

• A PhD degree in Materials science, materials chemistry, materials physics or equivalent degree in a closely-related area.
• Practical experience in machine learning or other forms of automation applied to experimental science.
• Demonstrated proficiency in coding, preferably using Python.
• Very good oral and written skills in English.

Additional qualifications

The following aspects are strongly meriting:

• A solid background synthesis and characterization of thin film materials
• Experience with workflow software and self-driving labs
• Demonstrated experience in design, testing and validation of hardware or software, applied in experimental science

Apply NowDeadline 11 August