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perturbation modelling. The ideal applicant brings not only strong technical skills, but also interdisciplinary knowledge on the subject. More precisely: PhD degree in computer science, machine learning
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Scotland Innovation Hub to provide a secure cloud computing platform for Federated Learning and Machine Learning model development, and clinical researchers from NHS Greater Glasgow and Clyde. The successful
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and Geophysics. Candidates should have a PhD in geology, geophysics or related field by the time of this appointment, be within 5 years of their PhD and have not held a permanent or tenured faculty
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. Eligibility is limited to candidates whose PhD will be conferred by August 1, 2026. Preferred Qualifications: Ph.D. in any area of physics by the start of the appointment period Passion for teaching and a
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-learning–based segmentation, species classification and lineage tracking workflows for multi-species time-lapse data Optimise models and pipelines for real-time performance, enabling adaptive imaging and
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% of the fellowship time to personal research. This is a one-year fellowship appointment, with the possibility of renewal for two additional years. Applicants must have fulfilled all the requirements for the PhD by
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minimizing computational and energy costs. The proposed approaches will rely on machine learning methods applied to image analysis, with the objective of enabling early identification of at risk areas and
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about the lab at: https://mbzuai.ac.ae/study/faculty/natasa-przulj/ and https://przulj-lab.github.io/ Qualifications PhD in Computer Science, Mathematics, Physics, Bioinformatics, or a related
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, including machine learning, computer vision, adaptive data modelling, and computational imaging. The objective is to develop state-of-the-art machine learning algorithms for solving ill-posed inverse problems
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- focusing on foundational logic and systems, strategic implementation and emerging technology. This is a full-time, non-tenure track position. Key Responsibilities: Teaching Teach four courses per semester