139 parallel-processing-bioinformatics positions at SINGAPORE INSTITUTE OF TECHNOLOGY (SIT)
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programme Is the Job related to staff position within a Research Infrastructure? No Offer Description FACULTY POSITIONS (AT ALL LEVELS) IN COMPUTER SCIENCE, COMPUTER ENGINEEERING AND INFORMATION SECURITY
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on a Pharmaceutical Innovation Programme Singapore research project where you will be part of a collaborative team exploring innovative ways of understanding extrusion processing for pharmaceutical drug
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Fellow or Research Engineer with strong expertise in Human-Computer Interaction (HCI) and social science methodologies to lead the user research and policy development, as part of an interdisciplinary team
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Interaction (HCI) and social science methodologies to lead the user research and policy development, as part of an interdisciplinary team investigating harmful user-generated content in virtual worlds as part
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. Degree in Infocomm, Computer Science, Cyber Security, Computer/Electrical Engineering, Information Technology or equivalent. Possessing a Master’s or PhD degree will be advantageous. Strong interest and
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liaison with vendors/suppliers. Work independently, as well as within a team, to ensure proper operation and maintenance of equipment Job Requirement Have relevant competence in the areas of Deep Learning
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. Any other ad-hoc duties assigned by supervisor. Job Requirements Bachelor’s degree or above in Electrical Engineering, Computer Engineering, or a related field from a recognised university. Strong
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publication record. Outstanding data analytics, mathematical, and computer modelling skills. Excellent interpersonal communication and oral presentation skills in English Self-driven and strong team spirit Open
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for the duration of the scheme. Application Process Applications are open throughout the year. Applications received before 15 January 2026 will be considered for intake 2026. Interview by
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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