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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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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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effective implementation of assessment priorities. This position collaborates with internal stakeholders on assessment and accreditation. How to Apply: Interested candidates may apply online at https
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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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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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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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About Us For more than 60 years, Kennesaw State University has been known for its entrepreneurial spirit and sense of community. A leader in innovative teaching and learning, Kennesaw State is
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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