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Context and Motivation Bilevel optimization problems, in which one optimization problem is nested within another, arise in a wide range of machine learning settings. Typical examples include
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are particularly interested in candidates who combine computational biology, data science, and machine learning/AI with deep biological insight. While wet lab activities are welcome, they are not mandatory. However
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. Responsibilities will include: Developing expertise in audiological test batteries Data wrangling, cleaning, and feature engineering Applying and implementing statistical or machine learning methods, depending
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, the adoption of Machine Learning (ML) techniques for the analysis of archaeological data sets is rapidly increasing [Mackenzie, 2017, Mesanza-Moraza et al., 2020, Bickler, 2021, Palacios, 2023]. ML applications
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of the project is to exploit such data to develop generative models for aptamer design. The candidate is expected to have a strong background in machine learning and statistical physics, with a real interest for
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Keywords: theoretical biophysics, machine learning, kinematics, (structural) biology. Context. Machine learning techniques have made significant progress in prediction of favourable structures from
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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g
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Leveraging the spatio-temporal coherence of distributed fiber optic sensing data with Machine Learning methods on Riemannian manifolds Apply by sending an email directly to the supervisor
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secure energy transmission and electrical power quality. Addressing these fields requires a forward-looking vision where digital technologies and dependable grids are integrated. The application of machine
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should have a graduate degree (Master 2 degree). Him/her scholar background should include: • statistical/machine learning, statistical inference, clustering, classification • deep learning, variational