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per year for 3.5 years. Lead Supervisor’s full name & email address Dr Massimiliano Fasi: m.fasi@leeds.ac.uk Project summary The growing importance of artificial intelligence is fostering a paradigm
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will lead to natural collaboration opportunities. The primary methods used in this project will be experimental, involving fluid characterisation and high-speed imaging experiments, using Phantom high
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flow visualisation and measurement techniques to study droplet impact under icing conditions to improve icing codes that aid in design and development of ice detection and mitigation system
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, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks Requirements: excellent university degree (master or comparable) in computer engineering or electrical
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of analysis and visualization software Very good written and spoken English skills are required Furthermore, experience in dealing with the ICON model and the scientific utilization of ICON data is an advantage
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purification is desirable but not necessary. Candidates with physics and physical chemistry are encouraged to apply. Knowledge of coding and image analysis is desirable but not necessary. As well as supporting
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for the modeling and simulation of 3D reconfigurable architectures e.g. based on emerging technologies (e.g. RFETs, memristive devices), and the evaluation with e.g. machine learning and image processing benchmarks
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Date 05/27/2025 Requisition Number PRN42057B Job Title Post Doc Res Assoc Working Title Post Doc Res Assoc Job Grade A00 FLSA Code Administrative Patient Sensitive Job Code? No Standard Hours per Week 40
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assaulting them. Second, technology has contributed to the creation of new forms of sexual harm, such as image-based abuse and deepfake technology. The implications of these developments are twofold. First
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(spoken and written). Preferred qualifications Prior experience with 3D cell culture, organoids, CRISPR-Cas9, or imaging-based phenotyping. Familiarity with transcriptomics or basic computational biology (R