{"id":3329731,"url":"https://alion.io/job/topsoe-principal-scientist","title":"Principal Scientist","company":{"id":1824606,"name":"Topsoe","domain":"topsoe.com","url":"https://alion.io/company/topsoe","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SuccessFactors","truth_index":null},"role":"Science & Research","role_family":"Science & Research","seniority":"lead","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Kraków, Poland"],"countries":["PL"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false}],"status":"live","first_seen_at":"2026-10-27T11:38:37Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-10-30T03:06:34Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"Connect catalysis research, experimental data and AI to generate insights that support materials understanding and development decisions.\nRole and impact\nYou’ll work in Experimental R&D, in close collaboration with Front-End R&D, catalysis researchers and modelling specialists. You’ll connect experimental and characterization findings with mechanistic understanding, data analysis and modelling-and help translate them into evidence-based development decisions.\nAs part of Topsoe’s AI for Materials effort, you’ll help test and demonstrate where AI and machine learning can improve catalyst development. The focus is on building practical in-house capability, learning from real Topsoe problems, and identifying the data and experimental workflows needed to take promising approaches further.\nResponsibilities\nAnalyse experimental and characterization data using Python or equivalent tools, including multivariate and statistical methods.\nDevelop reproducible workflows and connect characterization results to mechanistic understanding and development recommendations.\nApply 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.\nWork with domain experts to select informative experiments, interpret results and assess whether AI-supported approaches improve on an agreed baseline.\nWork with modelling specialists to integrate and interpret DFT and microkinetic modelling, bringing modelling insights together with experimental evidence.\nWhere relevant to the project, model spectra and link spectroscopic data to materials structure, composition and properties.\nCollaborate across Experimental R&D and Front-End R&D, communicating findings clearly and documenting methods and conclusions.\nContribute to repeatable in-house workflows and share methods and learnings across the AI for Materials community.\nHandle R&D data and intellectual property according to information-classification requirements, ensuring datasets, spectra, code and models are stored and shared securely.\nWhat you will bring\nA PhD or equivalent in catalysis, chemistry, chemical engineering, materials science or a related field.\nStrong catalysis knowledge and the ability to connect characterization with mechanistic understanding and development recommendations.\nData-analysis skills, preferably in Python, including multivariate or statistical methods and reproducible workflows.\nApplied experience using AI, machine learning or advanced data-analysis methods on scientific or engineering problems-and the ability to explain how the methods informed decisions, not just how a model was built.\nA collaborative, hands-on approach to working with experimentalists and computational specialists to design, interpret and validate studies.\nTechnical leadership, clear communication, good documentation habits and a collaborative approach.\nIt would be an advantage if you have\nExperience with Bayesian optimization, active learning, MLIPs, spectroscopy modelling, high-throughput experimentation or laboratory automation.\nExperience with DFT or microkinetic modelling, either hands-on or through working effectively with modelling specialists.\nWhy join us\nThis role offers the opportunity to help build Topsoe’s AI for Materials capability from within R&D. You’ll work on real catalyst-development challenges, connecting AI and data-driven methods with experimental evidence, modelling and domain expertise. Your work will help establish what AI can deliver today-and what data, skills and experimental capabilities Topsoe should build next.\nYour new team\nYou’ll join Experimental R&D and collaborate closely with Front-End R&D. The work brings together experimental research, catalysis, data analysis and modelling, so you’ll work across disciplines and with specialists to connect different kinds of evidence. You’ll help build capability that can be applied to Topsoe’s own R&D challenges.\nHow you can apply\nIf you would like to be part of a team of skilled and professional colleagues in an informal organization, please submit your documents in English.\nApplication deadline: 16 November 2026.\nYou do not need to attach a cover letter to your application - just include a few sentences in your CV explaining why you are applying. Please also avoid the use of photos in your CV. Both measures are to ensure a fair and unbiased process.\nWe welcome applications from all qualified candidates, irrespective of their gender, age, religion, disability, ethnic background, and geographical location. We encourage individuals from diverse cultures and countries to apply and contribute to our global team.\nTo ensure a secure and trustworthy work environment, our recruitment process may include background checks, requests for criminal records, and contact with references.\nTo headhunters: Thank you for your interest in our company. However, we would like to inform you that we have a dedicated internal team managing our recruitment process. We kindly request that you refrain from reaching out to us regarding potential candidates. Thank you for your understanding.\nAbout us\nTopsoe is a leading global provider of advanced technology and solutions for the energy transition. Built on decades of scientific research and innovation, we are working with customers and partners to drive energy resiliency and to achieve their sustainability goals.\nWe offer world-leading solutions for transforming renewable resources into fuels and chemicals, and we provide technologies needed to produce low-carbon and conventional fuels and chemicals as well as ensuring clean air.\nWe were founded in 1940 and are headquartered in Denmark, with around 2,500 employees serving customers all around the globe. To learn more, visit www.topsoe.com.\nAt Topsoe we strive towards having a safe work environment. 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