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Organization (RTO) active in the fields of materials, environment and IT. By transforming scientific knowledge into technologies, smart data and tools, LIST empowers citizens in their choices, public authorities
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, physics, mathematics, computer science, or related fields Demonstrated hands-on experience with machine learning techniques Strong programming skills (Python preferred) Experience analyzing time-series data
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optimization or related inverse design techniques. While this position does not involve developing AI models, it requires close collaboration with AI researchers to ensure data is appropriately structured for AI
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diverse academic backgrounds to contribute to our projects in areas such as: Network Security, Information Assurance, Model-driven Security, Cloud Computing, Cryptography, Satellite Systems, Vehicular
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information of 3 referees, including your PhD supervisor Join NUS and become part of our thriving community driving impactful research and innovation on the global stage. Only shortlisted candidates will be
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research. The candidate should have a PhD degree in Physics, Chemistry, Chemical Engineering or Materials Science and insight in catalysis. It is essential that the candidate has experience with synthesizing
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systems and research infrastructure. For further information, please contact Dr. Venkata SATAGOPAM (email address: ). Your profile A PhD in computer science, computational biology, bioinformatics
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systems and techniques including novel mouse models of metastasis, patient-derived samples, in vitro co-culture systems, molecular biology, multi-omics, and advanced microscopy approaches. The Labelle lab
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to follow this initial post. Applicants should hold a PhD (or equivalent) involving neuroimaging, cognitive neuroscience or a related field. Experience in MEG/OPM-MEG acquisition and data analysis is
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, Experience and Qualifications PhD in biochemistry, Biomedical Sciences or Chemistry. Mass spectrometry-based proteomics. Data analysis of large proteomics datasets. Experience in cell culture and molecular