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Strong employer brand but niche chemometrics and PhD requirement narrows applicant pool.
Highly domain-specific chemometrics and process expertise limit cross-industry transferability.
PhD, publications, and specialized chemometrics plus ML expertise create stringent mandatory filters.
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Develop and implement advanced chemometric, statistical, and AI models for complex chemical and process datasets to enable process understanding, optimization, and control.
Collaborate with chemists, engineers, and domain experts to translate chemical and process variability into analytical solutions and deploy scalable models in real-time or near-real-time environments.
Drive methodological strategy combining classical chemometrics, Bayesian learning, physics-informed AI, and machine learning to improve industrial and energy system performance.
PhD in Applied Statistics, Data Science, Chemometrics, Mathematics, or Engineering with experience in large-scale parameterization, process data modeling, and optimization.
Experience applying chemometric and multivariate statistical methods to analytical chemical data, including chromatography and spectroscopy data.
Work Experience Required: Significant experience delivering strategic business impact through R&D and consultancy projects; exact years not specified.
Strong expertise in AI, including experience with reinforcement learning, generative AI, or physics-informed machine learning applied to industrial process modeling.
Subject-matter expert operating at the intersection of mathematics, statistics, chemistry, engineering, and digital technologies with a strong focus on industrial chemical processes.
Proven ability to deliver scalable, robust modeling solutions handling noisy, sparse, and biased datasets for industrial or energy system applications.
Experienced in multidisciplinary collaboration from research through to deployment, with a strong publication track record in chemometrics, applied statistics, and AI methods for process optimization.