Catalysis AI Scientist: Data-Driven Materials Research

Topsoe; Kraków; On-site; PLN 180,000 - 240,000; Full time

Job summary

Topsoe's Experimental R&D team seeks a PhD-level researcher to connect catalysis experiments with data analysis and modelling, turning findings into evidence-based development decisions. You will work across Experimental and Front-End R&D, combining lab data, characterization results and mechanistic insights to guide catalyst optimization.

The role emphasizes applying Python-based analysis, AI and ML methods, and close collaboration with domain experts to design informative experiments and

Qualifications

• PhD or equivalent in catalysis, chemistry, chemical engineering, materials science or a related field.
• Strong catalysis knowledge and ability to connect characterization with mechanistic understanding.
• Data-analysis skills, preferably in Python, including multivariate or statistical methods and reproducible workflows.
• Applied experience using AI, machine learning or advanced data-analysis methods on scientific or engineering problems.

Responsibilities

• Analyse experimental and characterization data using Python or equivalent tools, including multivariate and statistical methods.
• Develop reproducible workflows and connect characterization results to mechanistic understanding and development recommendations.
• Apply AI and machine-learning methods to materials and catalyst-development problems. Depending on the use case, these may include Bayesian optimization, active learning, supervised or unsupervised learning, dimensionality reduction and uncertainty quantification.
• Work with domain experts to select informative experiments, interpret results and assess whether AI-supported approaches improve on an agreed baseline.
• Work with modelling specialists to integrate and interpret DFT and microkinetic modelling, bringing modelling insights together with experimental evidence.
• Where relevant to the project, model spectra and link spectroscopic data to materials structure, composition and properties.
• Collaborate across Experimental R&D and Front-End R&D, communicating findings clearly and documenting methods and conclusions.
• Contribute to repeatable in-house workflows and share methods and learnings across the AI for Materials community.
• Handle R&D data and intellectual property according to information-classification requirements, ensuring datasets, spectra, code and models are stored and shared securely.

Job description

Topsoe's Experimental R&D team seeks a PhD-level researcher to connect catalysis experiments with data analysis and modelling, turning findings into evidence-based development decisions. You will work across Experimental and Front-End R&D, combining lab data, characterization results and mechanistic insights to guide catalyst optimization.

The role emphasizes applying Python-based analysis, AI and ML methods, and close collaboration with domain experts to design informative experiments and