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Saelens team. Research Project In this research project you will develop probabilistic deep-learning models that automatically extract biological and statistical knowledge from in vivo perturbational omics
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unique atmosphere where there is expertise to dig deep into computational modelling, while remaining connected to the experimental side. This interdisciplinary atmosphere has been a main catalyst for many
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at the interface of machine learning, deep learning, geospatial AI, causal modelling, and digital health systems. Your Role You will develop the core AI and data-driven models that transform large-scale
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subgroups Support public health policy, prevention and hospital planning Provide meaningful feedback to patients, clinicians and policymakers The PhD will work at the interface of machine learning, deep
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in at least one major programming language, such as Python, is expected Familiarity with deep learning frameworks and modern NLP toolkits is an advantage Motivation to publish research results in
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Learning, particularly Graph Neural Networks, Transfer Learning, Deep Reinforcement Learning, and Transformer-based models, including hands-on implementation Strong understanding of machine learning models
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peripheral immunity shapes brain health and neurodegeneration, with a strong track record in myeloid biology, immune trafficking, and α-synuclein pathology. The clinical team at UZA brings deep expertise in
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, using fast cameras, radiation detectors, and deep-learning architectures capable of operating within milliseconds. It will also include collaboration with medical physicists and clinicians to integrate
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autonomous driving. Your profile Master's degree in Computer Science, Artificial Intelligence, Robotics, or related field Strong background in machine learning, deep learning, or computer vision Experience
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fundamentals of networking. Objectives: To achieve on-device spectrum sensing using on-board sensors of mobile BSs, empowered by embedded deep learning algorithms; to propose an analytical model for the cell