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decision making, while you will be capable to apply machine learning and computational algorithms of social choice. This post is associated with following projects: Embedding EDI in the Distribution
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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into products and services for Continental through close collaboration with its business units. Key Responsibilities: To independently undertake research in artificial intelligence, machine learning system, edge
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projects across the following areas: Spatial and Single-Cell Proteomics in Childhood Cancer Cell-cell communication & cellular fitness in CAR-T & CAR-NK therapy Deep learning & LLMs in mass spectrometry data
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integration and AI models tailored for fish behaviour, health, and stress signal analysis. Investigate and apply novel machine learning and deep learning techniques for pattern recognition, classification, and
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results. 3. BRIEF PRESENTATION OF THE WORK PROGRAMME AND TRAINING: - Develop machine learning-based models from data.; - Validate the developed models with real data.; - Publicize the work in international
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agents, including uncertainty quantification at the agent’s level. The project will bring together ideas from Statistics, Probability, Statistical Machine Learning, Statistics and Game Theory and is an
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. Demonstrated skills in longitudinal modelling and data synthesis, including machine learning, regression and GLM. Demonstrated ability to undertake high quality academic research in the field of dementia
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to field research. Experience with machine learning (ML) approaches to analyzing data. experience in student leadership or mentorship roles. Experience supporting the design and implementation of field
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Laboratoire de Physique des Interfaces et des Couches Minces (LPICM), UMR CNRS/École Polytechnique, | Palaiseau, le de France | France | 10 days ago
(denoising, Mueller matrix calculation/decomposition) and AI-based diagnostic algorithms using machine/deep learning. The primary challenge will be to deliver practitioner-relevant cervical images in under 0.5