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Field
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abort (or not engage) if the bright white lines that fit a defined and rigid expectation are not clearly visible. These systems use algorithms, rather than AI machine learning, to detect road markings and
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so, you will play a key role in advancing hydrogen turbine technology and supporting the global transition toward green energy and net-zero emissions. Why this project is unique A major highlight of
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, its wide networks of top international scholars, participation in international projects, and funding for conferences and training. We encourage applications from candidates of diverse backgrounds and
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-healthcare technologies•Security and privacy in computing systems and IoT•Intelligent wireless communications and networks•Systems-on-chip, FPGA/microcontroller design, real-time systems•Biomedical
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basis. Desirable: MSc in relevant field Experience with force plates and/or strain gauges Experience in systematic reviews, empirical research, and use of statistical analysis software Accredited
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including data engineers, clinicians, software developers, and lived experience experts to: Scope and integrate new datasets into an new web interface, focusing on mental health-relevant omics and real-world
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process. Address blind inverse problems by defining a network to learn distortion functions from data, informing the optimization in the learning process. Refine optimization and learning strategies
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body, university research committee, research team, etc. Build appropriate networks within and outside the team. Ensure that all data collection undertaken as part of the project is done so ethically and
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of network defenders, software developers and vendors, security researchers, AI practitioners, and policymakers. You'll get a chance to work with elite AI and cybersecurity professionals, university faculty
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stimulating and international research environmentA strong and dynamic network of international collaborators Primarily based at the VIB-UAntwerp with collaborative visits to the INJoint supervision by