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outstanding candidates to apply for a postdoctoral research position in Geometric Deep Learning, with a strong emphasis on applications to biology and scientific discovery. This unique research collaboration
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computational framework, integrated with deep reinforcement learning (DRL) methodologies for both gene-level and edge-level perturbation control, represents a significant advancement in the computational toolkit
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underlying sleep, a fundamental and evolutionary conserved behavior. We are studying the homeostatic and circadian mechanisms regulating sleep, and also have deep interest in understanding the functions
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on “Maternal Immune Activation” involving the development of novel artificial intelligence methods (graph and geometric deep learning, LLMs, …) working on methods for predictive multi-omics integration
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Ph.D. or equivalent degree in mathematics, physics, computer science, bioinformatics, or a related field Experience in developing deep learning models Ideally, prior experience in analyzing biological
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A PhD in immunology or related field with a deep understanding of mucosal immunology is required. Expertise working with primary human cells, intestinal tissues/organoids, murine models of intestinal
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Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic n...
Area of research: Laborkräfte Job description: Researcher / Postdoc for molecular investigations on microbial ecology in deep-sea polymetallic nodule fields (m/f/d) Background While some companies
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Nature Careers | Vancouver South Shaughnessy NW Oakridge NE Kerrisdale SE Arbutus Ridge, British Columbia | Canada | about 2 months ago
increasingly utilizes big data, satellite imagery, register data, and advanced methods such as deep learning and neural networks to address major societal challenges related to spatial inequalities and
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for the conversion of small and low energy molecules into advanced chemicals. The researcher will build up a deep understanding of the synthesised thin films, measure the electrocatalytic performances of the thin
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Python is required. Programming in C or C++ is a plus. Background in statistical genomics, longitudinal modeling, non-parametric statistics, machine learning and deep learning are preferred and encouraged