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relevant experience Curriculum vitae or résumé including academic background and any prior research or project experience (Optional) Code samples, GitHub profile, or links to prior projects
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(architecture principles and ML librairies) ML-literate: comfortable reading scikit-learn and PyTorch code and working with tabular machine learning workflows. Comfortable in a small team with direct ownership
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infrastructure-as-code (e.g. Terraform) across GCP and AWS, and maintain CI/CD pipelines, container build/registry workflows, and deployment automation so teams can ship safely and frequently. Strengthen
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bridging (e.g., JESD204C/eCPRI), and CUDA coding for GPU acceleration. Knowledge of RF calibration, test automation, and signal integrity in a research or prototyping context. Track record of peer-reviewed
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. Experience with embedded systems and hardware/software integration in a research or prototyping context. Experience with FPGA/RTL development and Xilinx tools, and CUDA coding for GPU acceleration. Knowledge
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systems, FPGA/RTL, and RF technologies, and CUDA coding for GPU acceleration is a plus. Strong leadership, communication, and documentation skills.
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delivery, no-code/low-code prototyping, data privacy and governance considerations Healthcare innovation, medical education, data analytics, procurement and vendor management experience would be advantageous
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administration, scoring, and interpretation of standardized child assessments Experience in qualitative research methods, including qualitative data collection, coding, and analysis Experience in research project
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translation: Code and analyse qualitative data, and turning findings into compelling visuals, narratives, and accessible outputs for stakeholders Writing and dissemination: Draft research reports, policy briefs
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responsibilities of the position include: Supporting the development and maintenance of teaching platforms and environments used in data science and AI courses. Managing code, data, and model lifecycle workflows