PhD Thesis: Self-Driving Lab in Water Electrolysis Cells, AIT Austrian Institute of Technology

Summary

AIT Austrian Institute of Technology invites applications for a funded PhD project to develop a self-driving laboratory that automates accelerated stress testing (AST) of water electrolysis cells. The successful candidate will design, build and operate a robotic testing platform, integrate FAIR-compliant data pipelines, and apply machine learning and closed-loop optimisation to link testing protocols with degradation and performance outcomes. This three‑year position is based at AIT’s Center for Energy in Vienna and is embedded in the AIT Research Acceleration Platform (ARA) – Energy Conversion and Storage Materials (ARA‑ESM).

PhD Thesis: Self-Driving Lab for Investigating the Effects of Accelerated Stress Testing in Water Electrolysis Cells

Designation

PhD Candidate / Ingenious Partner – Self‑Driving Lab for Water Electrolysis AST

Key Facts

Research Area

The project sits at the intersection of automated laboratory systems, electrochemistry and data‑driven materials research. It contributes to the ARA‑ESM platform’s drive to accelerate discovery and optimisation of materials and devices for clean energy conversion — in particular materials and workflows for water electrolysis and related energy technologies. Core activities include laboratory automation and robotics, high-throughput testing, materials characterisation, research data management (FAIR principles), and the application of machine learning (surrogate models, active learning, Bayesian optimisation) to design and interpret accelerated stress tests.

Location

The role is located at AIT’s Center for Energy in Vienna (Giefinggasse, 1210). You will be part of multidisciplinary teams within the ARA ecosystem and a wider AIT PhD community of international students and researchers.

Eligibility / Qualification

• Master’s degree (or near completion) in Mechatronics, Materials Science, Computer Science, Electrical Engineering, Physics, Chemistry or a related technical discipline. The master’s can be completed within three months after starting.
• Strong programming skills in Python and familiarity with data libraries such as Pandas, NumPy and SciPy.
• Practical experience or keen interest in laboratory automation, robotics, and hardware–software interfacing (e.g., REST APIs).
• Knowledge of machine learning frameworks and methods (e.g., PyTorch, TensorFlow) and experience with surrogate modelling or active learning is desirable.
• Understanding of electrochemistry and water electrolysis, and familiarity with materials characterisation methods is an advantage.
• Basic awareness of Research Data Management (RDM) and FAIR principles is beneficial.
• Capacity for independent scientific work, strong critical thinking, and motivation to publish and present at international conferences.
• Very good English (spoken and written); German is an asset.

Scholarship / Project Description

This funded PhD project will:

• Develop, assemble and operate an automated/robotic platform to run accelerated stress testing (AST) protocols on water electrolysis cells and to coordinate subsequent material characterisation workflows.
• Integrate automated data capture and ingestion pipelines that comply with FAIR principles in close collaboration with the team’s Data Steward.
• Apply machine learning tools — including surrogate models and active learning — to relate test conditions to degradation mechanisms and performance metrics.
• Implement closed‑loop experimental strategies (for example Bayesian optimisation) to plan and adapt test parameters autonomously based on real‑time results.
• Work interdisciplinarily with experts in robotics, software engineering, electrochemistry and materials science across the ARA ecosystem.
• Publish findings in peer‑reviewed journals and present results at international conferences.

As an employed PhD researcher you will receive a monthly gross salary, benefits, and access to the AIT PhD Community and related training and networking opportunities. AIT promotes an inclusive, family‑friendly environment and welcomes applicants from diverse backgrounds.

How to Apply

Please submit your complete application online via the AIT jobs portal. Your application should include:

• Curriculum vitae (CV)
• Cover letter outlining motivation and relevant experience
• Relevant certificates and transcript of records
• Proof of identity

Follow the official vacancy page to apply and for any further instructions on required documents. Shortlisted candidates may be asked for further documents or references.

Last Date for Apply

Applicants are advised to apply as soon as possible via the official link. For the most accurate and up-to-date deadline information, please consult the job listing.

Apply Link

https://jobs.ait.ac.at/Login/272800