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cancer biology, demonstrated by publications in high impact journals. The person would be well versed with various techniques used in the field of cancer immunology as well as having the ability to plan
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/Computer/Mechanical/Materials/Nuclear Engineering or related discipline. • Excellent foundation in materials processing • Hands-on experience with device and system characterization. • Experience in
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fields like civil engineering, organisational sciences and design sciences are combined. Our mission is guided by two research lines, each addressing two levels on which innovations are developed: 1
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(Kingdom of the) [map ] Subject Area: Advanced Nearest Neighbour Models for Active Matter Appl Deadline: 2025/08/10 11:59PM (posted 2025/07/14, listed until 2025/08/10) Position Description: Apply Position
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should have for this project, in addition to our standard entry requirements . Essential Criteria BEng/BSc/MSc in Mechanical Engineering, Applied Mathematics, Applied Mechanics, or related discipline such
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Research theme: Textiles How to apply: https://uom.link/pgr-apply-2425 How many positions: 1 This 3.5 year PhD project is funded by The University of Manchester and an industrial partner and the
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of Rock Engineering. The prospective candidate will be part of the Engineering Geology and Rock Mechanics research group at IGV but will also collaborate with other NTNU departments and societal
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learning environment. About John Crane Ltd: www.johncrane.com As a world leader in mechanical sealing, John Crane designs, manufactures, and supports technology used across energy and industrial sectors
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everything from basic science to strategic and applied research. The activities encompass research and education within materials, mechanics, physics, production, and industrial management and innovation
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
knowledge graphs for predictive insights. Design feedback mechanisms to deliver interpretable outputs such as alerts, recommendations, and confidence scores. Validate system robustness using real-world