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contributing to developing and implementing novel algorithms at the intersection of computational physics and machine learning for the data-driven discovery of physical models. You will be working primarily with
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academic and professional qualifications Proven research experience in the field of modelling and analysis of biological networks Solid foundation in mathematics and algorithmic design Strong programming
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classification for hyperspectral and fluorescence lifetime datasets. Optimize algorithms for batch processing and scalability, enabling high-throughput, automated analysis of large image datasets from fluorescence
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neurophysiological biomarkers with long-term neural activity recording and artificial intelligence algorithms to for conditions such as pain, Parkinson’s disease, sleep disorders, Alzheimer’s disease (AD), and
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health and disease, and experience in the algorithms used to analyze these datasets. The appointee will ultimately create an independent research effort with dedicated extramural funding that complements
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research environment focusing on integrating multi-source data and developing novel algorithms to address the challenges posed by global environmental change. You will focus on integrating experiments, field
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disease into specific subclasses. You will develop AI algorithms to train models that predict if individuals (from which we create circuits) are prone to develop disease and to identify conditions that have
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aims to develop a novel high-performance Particle-In-Cell (PIC) code for plasma physics simulations, leveraging the capabilities of exascale computing systems. By optimising PIC algorithms for modern
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will have the opportunity to investigate innovative solutions using machine learning algorithms and predictive modelling techniques in the context of a collaborative project with Goodyear Luxembourg (one
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Group. The position is offered within the scope of European Regional Development Fund project TARGETWISE. The candidate will be responsible for developing and implementing algorithms to analyse OMICS data