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Field
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modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
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learning PREFERRED QUALIFICATIONS: Experience using computational methods to analyze large-scale high-dimensional biomedical data relating to clinical information, genetics, genomics, radiomics, and/or
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data science, and/or public health or related fields including health services research, health informatics, computer scienceExperience in data analysis using statistical software & machine learning (e.g
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generation, media forensics, anomaly detection, multimodal learning with an emphasis on vision-language models, computer vision applications for space. Key responsabilities: Shape research directions and
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-series analysis, and environmental data analysis. • Demonstrated experience in artificial intelligence, including machine learning and deep learning, applied to hydrological or water-related systems
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that integrate multi-omics data to uncover mechanisms of disease, cellular resilience, and therapeutic response. The post holder will lead research applying large-scale machine learning and foundation models
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, large language models, or computer vision is preferred Knowledge, Skills and Abilities: · Teamwork: Ability to collaborate with others and contribute to a team environment · Communication
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at the interface of biostatistics, machine learning, and biomedical data science. This mentored postdoctoral position is designed to support the development of an independent research trajectory in methodological
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. Proficiency in SQL, Python/R, or similar tools; experience with big data platforms , machine learning, and data warehousing. Commitment to quality, integrity, confidentiality and compliance. Excellent
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of AI and Data Science : Machine and deep learning, NLP, BDI (Belief-desire-intention) systems, and Large Language Models (LLMs). Expertise in design and very good programming skills (Python, Pytorch