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experiments. The objective is to develop Bayesian causal models and neural networks capable of identifying relevant causal relationships between instrumental parameters and observed anomalies. The work will
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for high-dimensional learning and generative modeling. Research interests span representation learning, statistical inference, privacy, and generative models with applications in physics, audio, vision, and
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experimental parameters (time, temperature). To optimize these parameters, active learning techniques based on Bayesian optimization will be applied. In situ or ex situ characterizations (FTIR, ¹¹B/¹H NMR, HP
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causal inference methods (e.g., directed acyclic graphs) would be an asset Fluent in reading, writing, and speaking scientific English Required Skills : Experience in data management and analysis of cohort
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experiments and analysing the results in order to draw up recommendations), 3/ Eye-tracking and insitu evaluation (Using eye-tracking tools to evaluate physical and urban routes, Contributing to the analysis
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integrated Optics / Photonics or a related discipline. ● A strong track record demonstrating excellence and creativity. ● Experience in design and simulation of integrated photonics components, including
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PhD in chemistry, biochemistry, biophysics or structural biology (or related discipline) and has experience in protein expression and purification as well as a proven track-record in solution NMR
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tomography (SPECT) is an imaging technique that tracks the 3D distribution of a radioactive tracer administered to a patient to monitor certain biological functions. The long acquisition times (10-40 minutes
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skills, and proven track-record in at least two of these three areas: fluid mechanics, active matter, and reinforcement learning. Website for additional job details https://emploi.cnrs.fr/Offres/CDD
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particular focus on digital methods and tools. The C²DH's ambition is to venture off the beaten track and find new ways of doing, teaching and presenting contemporary history of Luxembourg and the history