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engineering a strong background in digital design, hardware description languages (e.g. Verilog, VHDL, SystemC), reconfigurable architectures (e.g. FPGA, CGRA) What we expect from you: above-average degree
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. Experience: Experience leading the design, development and publication of innovative projects in AI/ML solutions. Experience building agentic systems and applying them to scientific problems. Proven record
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University Hospital, Duke Regional Hospital, Duke Raleigh Hospital, Duke Health Integrated Practice, Duke Primary Care, Duke Home Care and Hospice, Duke Health and Wellness, and multiple affiliations. Be You
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of work experience in injury or resuscitation related research. Prior experience in writing/submitting grants. Demonstrated expertise in relevant research design methods. Compensation and Benefits UC offers
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Present research findings at conferences and in scientific publications, and complete a dissertation within the project period The successful candidate is expected to enter a plan for the progress
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Requirements: excellent university degree (master or comparable) in computer engineering or electrical engineering a strong background in digital design, hardware description languages (e.g. Verilog, VHDL
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strategies for robust statistical models. The project’s scope will be tailored to the candidate’s expertise, offering opportunities for innovation and impact. The successful applicant will join a dynamic
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researcher to investigate the institutional dimensions of managing public space — a crucial but often overlooked area in urban and regional development. While much attention goes to the design and planning
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of the research plan through conducting of experiments, sample and data analysis and write up of results for scientific publication are part of the PhD process – a journey to become an independent researcher
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innovation and active participation. For TUD diversity is an essential feature and a quality criterion of an excellent university. Accordingly, we welcome all applicants who would like to commit themselves