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9th April 2026 Languages English Norsk Bokmål English English PhD Fellowship in Surrogate Modelling of Fluid Flows using Deep Learning Apply for this job See advertisement Job description The
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. Research Project: Current dose optimization strategies for oncology therapeutics rely primarily on clinician-reported adverse events (CTCAE) in exposure-response (ER) analyses, which may inadequately capture
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Singapore’s AI-for-Science ecosystem. Key Responsibilities: The candidate is expected to conduct the following research works Develop and optimize multimodal LLM/MLLMs for downstream tasks (adaptation
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optimizing fabrication and testing processes for new devices. Qualifications PhD degree in Electrical Engineering, Materials Science, Mechanical Engineering, Biomedical Engineering, or a related field with
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optimizing fabrication and testing processes for new devices. Qualifications PhD degree in Electrical Engineering, Materials Science, Mechanical Engineering, Biomedical Engineering, or a related field with
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to discover and optimize novel therapeutics, with future research focusing on developing next-generation proximity-inducing drugs. Key Responsibilities: Propose hypothesis-driven target molecules based
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extensive use of 3D printers for fabricating custom parts, modifying and optimizing 3D printer hardware and firmware to meet experimental requirements, and integrating these systems into functional laboratory
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outcomes into impactful publications. Key Responsibilities: Design and synthesize novel monomers and polymers Develop and optimize membrane fabrication techniques Conduct advanced material characterization
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optimization for monoclonal antibody discovery. Conduct mouse monoclonal antibody generation using hybridoma technology. Carry out antibody characterization (binding affinity, specificity, epitope mapping
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(iii) multimedia forensics & biometrics. Resources at the ROSE Lab consist of NTU faculty, researchers, PhD students, and visiting researchers from other institutions from around the world, as