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operational data and machine learning. You will be based at UCL mechanical Engineering, and collaborate with industry and port partners on system design, prototyping, and lab-based trials. Key responsibilities
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to learning opportunities, local research networks, and access and facilities. Candidate profile: Applications are invited from outstanding graduates who meet the following criteria: BSc, including synthetic
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rapid and significant shifts in power consumption, next-generation high-performance computing (HPC) platforms used in machine learning, big data and artificial intelligence (AI) based applications (CPUs
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essential—what matters most is curiosity, enthusiasm, and a willingness to learn. You'll be part of a vibrant research group with strong national and international links, and have the opportunity to travel
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, the student will collaborate with researchers who apply data assimilation and machine learning methods to the developed models. Your responsibilities: Analysing a global compilation of paleomagnetic sediment
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) with excellent grades in computer science, materials science, physics, or a related discipline Practical experience in data science, including the application of machine learning (ML) methods or large
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application of machine learning-based algorithms for the identification of antibiotics-associated proteins and antimicrobial peptides Perform and support experimental studies across the METAMIC project
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achievements in the field of physical sciences documented by publications in renowned journals 2. Additional Requirements: 1) statistical analysis of large data sets 2) knowledge of machine learning 3
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% and annual vacation pay of 8%. High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning
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Leiden, which will maximize access to learning opportunities, local research networks, and access and facilities. Candidate profile: Applications are invited from outstanding graduates who meet the