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Field
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aims to improve electrodialysis (ED) for REE separation by developing advanced membranes and integrating AI-driven optimization techniques. By combining materials innovation with machine learning
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for reproducible HPC environments. Experience with CUDA-level optimization or debugging hardware-specific performance differences. Basic knowledge of protein structure, folding, or biophysics. Physical Demands
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strategies Integrated Coastal Zone Management (ICZM) Water information systems, knowledge management, and decision-support tools Forecasting, monitoring, and early warning systems Environmental sustainability
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to the UrEco 2030+ project: “Optimizing Urban Ecosystem Services Model for Urban Climate and Biodiversity in Singapore towards 2030 and Beyond.” This position requires experience in Geographic Information
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: Designing, synthesizing, and optimizing molecules targeting GPCRs (cannabinoid receptors; allosteric and orthosteric) and ion channels (α4β2 nAChR, α7nAChR, α9* nAChR, GIRK1/2, GIRK1/4, etc.) Conducting
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learning based optimization algorithms, human and AI coordination in best decision making for urban transportation related problems. The role will focus on developing generic frameworks and innovative
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structure–activity relationships (SAR) to optimize the potency, physicochemical and ADME properties, pharmacokinetics, safety, and in vivo efficacy of lead molecules. In collaboration with other scientists
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materials for new products, and optimizing manufacturing processes Responsibilities R&D: Lead material selection, development, and characterization for applications in electronics, aerospace, or biomedical
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of thermoplastic polymers; 2. Experience in compounding and formulation of polymeric materials; 3. Demonstrated ability to optimize processing parameters; 4. Experience in the physicochemical, thermal, and
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across all aspects of medicinal chemistry, including: Designing, synthesizing, and optimizing molecules targeting GPCRs (cannabinoid receptors; allosteric and orthosteric) and ion channels (42 nAChR