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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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(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 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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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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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
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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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(row/column generation; heuristics/meta-mechanistics). You'll be able to deploy your knowledge and skills to produce high-quality results for internal, national and international research projects