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future autonomous instrument control and self-directed experimentation will be developed, recognizing the challenge presented by the integration of multiple complex systems. Coding and user interface
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materials systems at the molecular level with machine learning. The PhD Student will work with tumour sections to develop multiple instance learning and weak supervision / spatial transcriptomics models
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3-year PhD studentship: Scaling-Up Functional 3D Printing of Devices and Structures Supervisors: Professor Richard Hague1 , Professor Chris Tuck1 , Dr Geoffrey Rivers1 (1 Faculty of Engineering) PhD
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The Faculty of Engineering, Department Industriële ingenieurswetenschappen, is looking for a PhD-student with a doctoral grant. More concretely your work package, for the preparation of a doctorate
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qualifications As our new colleague in our research team your job will be to develop novel computational frameworks for machine learning. In particular, you will push the boundaries of Scalability, drawing upon
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, spanning domains from automotive and avionics to healthcare, increasingly rely on distributed and multi-layered control architectures. These systems comprise interconnected computing nodes, actuators and
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. Requirements: university degree in Computer Science, Physics, Engineering, or a related field strong background in relevant research areas genuine interest in research and a high level of enthusiasm
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challenges of quantum technology translation and enjoy problem-solving across multiple domains. Funding and Eligibility This studentship covers tuition fees and stipend for UK students only. Applications from
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will inform future race strategy and live race tactics. Multiple factors influence the strategy and tactics in professional road cycling, and these have changed significantly since the COVID-19 pandemic
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As Austria's largest research and technology organisation for applied research, we have set ourselves the goal of making substantial contributions to solving the major challenges of our time