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Deadline: 31 August 2025 A fully funded 3.5 year PhD position is available to work on the project titled “Scalable benchmarking for digital quantum computers based on blind testing”. This position
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Ecole Polytechnique Federale de Lausanne, Unsteady Flow Diagnostics Laboratory Position ID: Ecole Polytechnique Federale de Lausanne -Unsteady Flow Diagnostics Laboratory -PHD [#30284] Position
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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MRI, echocardiography, and CT. Another promising approach is the use of cardiac digital twins—mathematical models that simulate a patient’s heart to allow the design and in silico testing of novel
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institutes in Berlin which are funded by the federal and state governments. The research institutes belong to the Leibniz Association. WIAS invites applications as PhD Student Position (f/m/d) (Ref. 25/11) in
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limited. We are offering a PhD scholarship for a student to develop ambitious new machine learning strategies for generating AI-ready data. You will work at the frontier of active learning and ML-guided
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ultrasonic sensors for real-time seal gap measurement. Combine experimental research and mathematical modelling to pioneer innovative sealing solutions. What We Offer Full 4-year Funding for UK applicants
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which are funded by the federal and state governments. The research institutes belong to the Leibniz Association. WIAS invites applications as PhD Student Position (f/m/d) (Ref. 25/11) in the Leibniz
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Biology, Physics, Applied Mathematics, Computer Science, Bioengineering, Systems Biology or a related field. Proficiency in modelling using differential equations is required. Candidates must have
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sources such as (i) atmospheric models, (ii) satellite remote sensing, (iii) land use information, and (iv) meteorological data. The aim of this PhD is to develop and implement models for integrating data