Scholarship postdoc

Postdoctoral Scholarship at Uppsala University

The Department of Chemistry – Ångström conducts research and education in the field of chemistry. The department has more than 270 employees and an annual turnover of 300 million SEK. Our six divisions carry out high-impact research at an international level. We host a large number of externally funded projects, often in collaboration with international partners, and we continue to see strong growth in our research

area. The department also contributes extensively to education within engineering and master's programs. More information is available on our website:

.

Role Description

We are offering a 1+1 year postdoctoral scholarship at the Department of Chemistry – Ångström, Uppsala University, focused on the development of
machine learning methods for mechanistic electrochemistry
. The project aims to combine electrochemistry with physics-informed and explainable AI to solve one of the central challenges in the field: extracting reaction mechanisms and kinetic parameters directly from cyclic voltammetry data.

Electrocatalysis underpins key technologies such as CO₂ reduction, hydrogen production, and sustainable chemical synthesis, yet mechanistic interpretation of electrochemical data remains largely manual and heuristic. In this project, we will shift from forward modeling to the inverse problem, developing ML models that infer mechanisms from experimental data in a physically consistent and interpretable way.

The postdoctoral researcher will work at the interface of electrochemistry, data science, and physical modeling, contributing to the creation of a prototype tool for automated, mechanism-aware analysis of CV data.

The postdoctoral researcher is expected to:

• Develop and implement machine learning models for classification and interpretation of cyclic voltammetry data
• Generate and curate datasets based on simulated and experimental voltammograms
• Integrate physical constraints into ML models (physics-informed machine learning)
• Apply and develop explainable AI methods to ensure interpretability of model predictions
• Present research findings at group meetings, international conferences, and in peer-reviewed journals
• Contribute to the development of a user-friendly proof-of-concept tool for CV analysis

Qualifications

• PhD in chemistry, physical chemistry, physics, chemical engineering, data science, or a related field
• Strong interest in interdisciplinary research at the interface of machine learning and physical sciences

Experience in one or more of the following is required:

• Machine learning / data science / scientific computing
• Electrochemistry or physical chemistry

Experience in any of the following is an advantage:

• Physics-informed machine learning or scientific ML
• Time-series analysis or signal processing
• Explainable AI methods
• Cyclic voltammetry or electrocatalysis

Backgrounds in either
ML/data science
or
electrochemistry
are equally welcome; candidates with strong expertise in one area and motivation to learn the other are strongly encouraged to apply.

Deadline for application:
February 14, 2026

Applications will be evaluated continuously, so early applications are strongly encouraged.

To apply,
please send:

• CV including at least 2 references
• Motivation letter

to Alina Sekretareva, , Subject line: "Postdoc application: ML in echem"