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non-redundant collection of data for each new case of cancer (excluded non melanoma skin cancer). The RNC is aimed at providing an objective analysis of cancer evolution in Luxembourg, and enables
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, or examples, these aspects are of utmost importance and need to be explored to provide convincing and well-grounded arguments [1]. This PhD program will propose to explore advanced methods to detect implicit
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controlled via structural phase transitions or external fields. The successful candidate will develop and apply a range of theoretical and computational methods based on first-principles electronic structure
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liquids stand at the forefront of condensed matter physics. By joining our team at École Polytechnique, you will be at the heart of these discoveries, developing a next-generation thermal transport
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experimental data and is testable across multiple unlearning scenarios. For this we plan to apply for the first time Spiking Neural Networks (SNNs) to the modeling of unlearning. SNNs have recently shown
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available to work on computational models of artificial evolution for aptamer design, expected to start between November 2025 and February 2026. Aptamers are often obtained through rounds of in vitro
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to drive progress in translational medicine, improving diagnostics and healthcare solutions. For more information, please visit our page. You will be the scientific liaison for a team developing secure and
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into biologically relevant hypotheses about the physiological and pathological mechanisms of mental processes. Responsibilities: Develop and implement relevant computational models relative to specific behaviors and
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candidate will create her/his own team for developing and implementing innovative AI-based approaches to address key challenges in structural biology, including but not limited to: Designing synthetic
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The successful candidate will develop computational approaches to discover, model, and develop therapeutic strategies. Examples of potential approaches include: -Network Modeling: Creating