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of wireless communications, edge computing, and machine learning, and who is eager to translate theoretical insights into practical systems. Key Responsibilities Derive and analyse closed-form mathematical
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computer vision and vision-language models Experience with ML evaluation metrics and benchmarking Proficiency in Python and deep learning frameworks (e.g., PyTorch) Interest in applied, industry
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, microbial cultures, and cleaning validation samples. Develop data analysis pipelines for Raman spectral classification, potentially integrating machine learning methods. Research & Project Responsibilities
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Engineering, Biomedical Engineering, Medical Imaging, Signal Processing, Applied Physics, Computer Engineering, or a closely related discipline. Strong background in at least one of the following: ultrasound
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Engineering, Computer Engineering, HealthTech, MedTech, Product Development, Clinical Engineering or related disciplines with relevant experience in engineering, prototyping, product development or healthcare
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Engineering, Computer Engineering, HealthTech, MedTech, Product Development, Clinical Engineering or related disciplines with relevant experience in engineering, prototyping, product development or healthcare
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Computer Science, Artificial Intelligence, Software Engineering, or a related field. Strong programming proficiency in Python and/or C++. Demonstrable experience with machine learning frameworks (e.g., PyTorch
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operations. Process control: process modelling, control, and optimization, with applications in chemical and pharmaceutical manufacturing; data-driven modelling and machine learning applications in process
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As a University of Applied Learning, SIT works closely with industry in our research pursuits. Our research staff will have the opportunity to be equipped with applied research skill sets
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basic experience in machine learning or computer vision libraries; familiarity with Vision-Language Models (e.g., CLIP, BLIP) or scene-graph inference is a plus. Key Competencies Strong software