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learning models) in these tasks. These investigations include the feasibility, practicality and success evaluation of prototype implementations. More generally, the PhD thesis is part of a large initiative
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We are looking for a highly motivated PhD candidate interested in AI-based methods, including machine learning and language technologies, for the integration and analysis of clinical, advanced data
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We are seeking a highly motivated PhD student to perform fundamental research and to conceive truly sparse solutions (on both, CPU and GPU) for dynamic sparse training, aiming to cut the training
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candidate will perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon
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-FNR PEARL Research Grant in the area of Information Systems Engineering, and, depending on interest, in fields, such as Generative AI & Machine Learning, Data Privacy, Cyber Security, Digital Identities
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! Education · PhD in computer science, engineering, applied mathematics, physics, or another STEM discipline. · Demonstrated experience with mathematical and numerical optimization methods
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. This is particularly relevant for high-demand engineering structures, where local stress concentrations often drive damage initiation and limit service life. The PhD will focus on a staggered bottom-up
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SD-26045-RESEARCHER IN ADVANCED PLASMA-ASSISTED DEPOSITION PROCESS DEVELOPMENT FOR CATALYTIC THIN...
publications and consortium meetings Collaborate with consortium partners. Is Your profile described below? Are you our future colleague? Apply now! Education You hold a PhD in Materials Science, Applied Physics
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Computing, Cryptography, Satellite Systems, Vehicular Networks, and ICT Services & Applications. Your role We offer a fully funded PhD student position within the Trustworthy Software Engineering (TruX
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understand, explain and advance society and environment we live in. Your role The University of Luxembourg invites applications for a fully funded Ph.D. position in machine-learning force fields (MLFFs