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an exceptional opportunity to conduct cutting-edge research at the intersection of machine learning, healthcare, and computational modelling, contributing to real-world clinical impact. What is offered
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(e.g. 2.1 or above undergraduate degree in physics, mathematics or computer science) Willingness to adapt and work across different disciplines Ability to work independently and cooperatively Commitment
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networking with colleagues and students; planning and organising research resources and workshops. Successful applicants will have or be near to completing a PhD in computer science, information engineering
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degree in Computer Science, Math, Statistics or Engineering programs. Ph.D would be an asset. Knowledge of classification algorithms, vision-language models (VLM) and Hugging Face Transformers is an asset
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projects are multidisciplinary in nature, and the ideal candidates are comfortable with moving outside their field to understand both molecular biology and computational methods. We rely heavily
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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Fields: Open across ten schools covering disciplines such as Engineering, Construction, Computer Science, Law, Business, Arts and Creative Industries, Architecture, Health, and Education Duration: Standard
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Qualification: The candidate should possess a MSc. Degree or equivalent in Engineering, Computer Science, or related fields. Experience: The ideal candidate should have some knowledge and experience
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machine learning, computer vision, and materials science. The focus of this position is on development of neuro-symbolic models for the effective behaviour of the complex microstructure of recycled
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, their achievements and productivity to the success of the whole institution. At the Faculty of Mathematics, Institute of Scientific Computing, within the Dresden Center for Computational Materials Science (DCMS