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Field
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analysis, algorithm development, simulation, and hardware-in-the-loop experimentation. The Research Fellow will work closely with the Principal Investigator and collaborators/students across NTU Singapore
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primary deliverable of this position is a theoretical and methodological template that we call Action-Oriented Modeling. Rather than delivering finished algorithms, the template specifies how to build
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lead the design, statistical optimisation and validation of assays for clinically relevant bladder cancer targets. Their central objective will be to develop an algorithmic workflow to detect new target
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on a project in the area of cross-border electricity market. The role will focus on cross-border electricity market design, market risk analysis, bidding strategies, and relevant optimization algorithms
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analysing algorithms and metrics for ensuring fair treatment of groups defined by protected attributes (e.g. gender, age, ethnicity, …). Theoretical relations between fairness notions: studying
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estimation algorithms that operate reliably in challenging environments (e.g., low texture, illumination variation, dynamic scenes, or sensor degradation). The research will integrate model-based estimation
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algorithms into the real development progress Preparing academic publications such as patent applications and research papers Contributing to quarterly and annual report writing and presenting Job Requirements
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differentially private learning, its connections to replicability of algorithms, and algorithmic fairness. Basic Qualifications Candidates are required to have a doctorate or terminal degree in Computer Science
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circulation of radical content, how algorithms amplify or suppress such content, and how these dynamics can be regulated through policy and governance. The Research Fellow is expected to collaborate with
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-driven video understanding of consumer facial care behaviours. Work with PI and company to develop the AI solution Develop novel algorithms for: Fine-grained video understanding Concept learning Temporal