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at the interface of machine learning and computational neuroscience. The candidate will be part of the COATI joint team between INRIA d’Université Côte d’Azur and the I3S Laboratory. Project The candidate should
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hyperparameter optimization, meta-learning, and adversarial training. The general bilevel problem can be written as: min F (x, y∗(x)) where y∗(x) = arg min f (x, y), d
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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, copyrighted, or biased. By studying brain data recordings and building computational models that mimic real populations of neurons, the project aims to uncover active unlearning: how the brain learns
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the exact calculation of the square-root and inverse square-root of the source distribution covariance matrix. This approach offers analytical and computational advantages in comparison to existing methods
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(large scale heterogenous data synthesis, meta-analytic studies, conceptual synthesis) Experiences and interests in shaping modern team science research and interest in super-visioning & coordinating
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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
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their research through investigations at national, regional, or local scales - preferably in the vulnerable regions such as the Global South. Specifically, synthesis approaches such as meta-analytic tools
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also expected to demonstrate the ability to ground their research in national, regional, or local contexts. Strong emphasis will be placed on synthesis approaches, such as meta-analytic techniques and
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well as computational modeling. The development and numerical implementation of novel methods has become a key issue in modern oncology, both in terms of understanding the biology of cancers and for medical oncology