153 Computer-Science-"https:"-"https:"-"https:"-"https:" positions at SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
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Assistant Professor / Associate Professor / Professor in Aerospace MRO (Smart Maintenance & Repair Systems) The Singapore Institute of Technology (SIT) is Singapore’s first University of Applied
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Institute of Technology (SIT) is Singapore’s first University of Applied Learning, offering industry-relevant degree programmes that prepare its graduates to be work- and future-ready professionals. Its
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creative design with engineering feasibility to develop manufacturable, real-world product and service solutions. Industrial Design Leadership Set Strategic Direction: Lead the vision and standards
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mentor for existing SIT domain faculty through joint projects and co-supervision mechanisms. Qualifications: PhD in Robotics, Mechanical/Electronics Engineering, Computer Science, or a relevant domain with
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Professional Officer in Food Analysis The Singapore Institute of Technology (SIT) is Singapore’s first University of Applied Learning, offering industry-relevant degree programmes that prepare its
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innovation, the incumbent will drive strategic partnerships, programme growth, and real-world project translation. This role combines hands-on design and research leadership with operational oversight
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healthcare-sector leadership track record, preferably at Assistant Director level or above, with proven experience in managing a business unit, service line, programme office, or multi-disciplinary delivery
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that are relevant to industry demands while working on research projects in SIT. About the DIGNIFIED programme The DIGNIFIED programme is dedicated to developing elderly-friendly, textured modified foods with
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diverse team of faculty members, engineers, and industry partners, including renowned researchers at the College of Computing and Data Science (CCDS) at NTU. Technical skills, natural curiosity, versatility
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Engineering, Computer Science, Data Science, Statistics, or equivalent. Strong theoretical background in statistics and machine learning. Knowledge of the basics of federated learning and causal inference is