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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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II is looking for a part-time (30 hours per week) PhD-Position: Machine Learning / Medical Imaging (m/f/x) (with immediate effect). This position is offered for a duration of 3 years. Join the AICARD
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cutting-edge research in areas such as pattern recognition, automation science, complex systems, AI for Science, robotics, machine learning, computer vision, natural language processing, biometrics, medical
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machine learning, generative models, or data science methods; Engaging with public outreach activities and supporting MSc and PhD students’ supervision as requested. Job requirements Essential Requirements
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principles that regulate host-pathogen interactions and feedback, using a combination of quantitative imaging, microfluidics, statistical analysis and machine learning tools. A specific focus will be put
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(I3S), Sophia Antipolis Hosting lab: I3S & INRIA UniCA Apply by sending an email directly to the supervisor: emanuele.natale@univ-cotedazur.fr Primary discipline: Machine Learning Secondary discipline
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Postdoc Position (f/m/d) for any of the following topics: Combining non-equilibrium alchemistry with machine learning Free energy calculations for enzyme design Permeation and selectivity mechanisms in
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in writing. • Computer literacy (MS-Office; Imaging Software). • Basic experience in academic writing. • Didactic competences / experience with e-learning. • Excellent command of written and spoken
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Senior Scientist / Group Leader on Bioinformatics / Computational Biology on RNA Regulation in Disea
, and validate computational findings Apply machine learning and statistical modeling techniques to identify patterns and predict functional impacts of RNA modifications Contribute to publications
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at the intersection of statistics, machine learning, data analytics and modern AI algorithms. This includes, in particular, statistics for high-dimensional and complex data, stochastic optimization