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applications. The project integrates: Computational Fluid Dynamics (CFD) and multiphase flow modeling Radiative heat transfer Machine learning and reduced-order modeling Data-driven optimization for industrial
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/training. Preferred Qualifications: Demonstrated skills (or ability to learn quickly) in any of the following: programming (especially Python), data science, machine learning, and statistics. Previous
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perform 3D single-particle tracking and establish pipelines to characterise the particle motion using a combination of established tracking algorithms and machine-learning-based approaches. Additionally
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duties as assigned. REQUIREMENTS: REQUIRED: PhD in in computer vision, machine learning, artificial intelligence, or a closely related field. Strong programming skills. Strong background in machine
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skills, including proficiency in statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets
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-performance computing. SLU provides access to extensive datasets that can be used to develop machine learning methods and automated analyses relevant to the position. Long-term datasets are available from, i.a
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‑of‑the‑art computational featurisation with experimental reaction‑kinetics data to build a machine‑learning platform capable of predicting catalyst performance. This is an exciting, highly collaborative
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statistics, scientific programming, and/or modelling. We especially welcome candidates interested in applying AI and machine learning to analyse heritage datasets. What we offer: • A stimulating
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Assistant Professor in Marine Biology & Ecology - Biomedical Science or Quantitative Systems Ecology
ecologist working in coastal systems, who applies modern approaches in causal inference, experimental ecology, spatial modelling, and data science, including the use of machine learning to produce rigorous
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University Department of Physics invites applications for a full-time non-tenure track appointment at the rank of Instructor to primarily teach introductory physics in the fall 2026 and spring 2027 semesters