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, imbalanced-data, or distribution-shift settings. Job Requirements: PhD in Computer Science, Data Science, or a closely related field. Strong research background in machine learning and artificial intelligence
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cardiovascular MRI, cardiac diffusion tensor imaging, and machine learning or deep learning methods applied to imaging is highly desirable. You will be a collaborative and motivated researcher, capable of working
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in computer science, statistics, machine learning, electrical engineering, or related discipline with eight (8) years of experience; OR MS in the same fields with one (1) year of experience; OR PhD in
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the essentials of linear algebra, calculus, probability, statistics, algorithms, and data structures. The goal is to provide a foundational understanding of machine learning and deep learning methods
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domains (e.g., automotive, human-computer interaction), where efficient on-device processing is essential. We are looking for a highly motivated PhD researcher with an interest in hardware-aware
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scheduling and resource allocation problems for machine learning pipelines deployed in safety-critical cyber-physical systems. Close collaboration with PhD students involved in the project. Participate in
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confirmed PhD commencement date: To be confirmed Project Key Words: Machine Translation, Natural Language Processing, Terminology Translation, Agentic RAG Post summary We are looking for a candidate for a
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research. This role will strengthen the university’s capabilities in AI, machine learning, and data science, with applications aligned to one of our priority research areas: Health and Life Sciences; Defence
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algorithms and system frameworks that optimize cost, performance, and scalability. The role focuses on leveraging machine learning and reinforcement learning to enhance storage and service efficiency under
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for this PhD proposal should have the following qualifications: M.Sc. degree in electrical engineering, mathematical engineering, acoustics, machine learning or similar; Solid mathematical and analytical skills