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++, or Go, and frameworks like PyTorch or TensorFlow, is highly advantageous. Experience in developing and deploying machine learning models, particularly in natural language processing (NLP) and large
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the short to medium-term burden of infectious diseases across large spatial scales using high-frequency data. Key Responsibilities: Develop models to understand the epidemic potential and instantaneous
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devices, smartphones, questionnaires and computer-based behavioral tests to collect large-scale, multi-sensor data streams. The research assistant will join a dynamic and multidisciplinary team conducting
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in science education (preferred) Experience in statistical analysis (preferred) Skills in prompt engineering or proficient use of large language models Strong communication, organisational, writing
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using large language models, disease endpoint coding initiatives, and creation of common data model (CDM). He/she will also be expected to support/lead high-quality research in chronic disease
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outputs across Projects 3-1 and 3-2 Manage and process large sets of operational data for case study analysis Contribute expertise in power grid optimisation to enhance planning models Participate in
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FEM matches experimental data Use FEM to get large amount of data for machine learning Excellent learning ability Excellent communication ability Strong interest in machine learning Candidate should be
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with time-series data. The role involves conducting research on multimodal learning and leading technical development efforts within the project. Qualifications Applicants should meet the following
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. Technical Skills: Proficiency in medical statistics, data science, and applications across various domains. Skills in processing and analyzing large datasets, data wrangling, visualization, big data analysis
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year (initial contract), extendable to 2–3 years based on performance Duties and Responsibilities The appointee will support the project leader in the following areas: Big Data Analytics & Computational