258 machine-learning "https:" "https:" "UCL" "UCL" Postdoctoral positions at Nature Careers
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The Chiu Lab (https://chiu-lab.org/ ) focuses on developing state-of-the-art multimodal AI models for cancer research. Our recent publications have been recognized by both the cancer research and
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in dynamical systems modeling (ODEs) and machine learning and very strong programming skills (Java, Python). A background in evolutionary genomics research is a strong plus, as is previous experience
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: https://obgyn.uchicago.edu/research/griffith-laboratory Required Qualifications: Qualifications needed for this position include a PhD in clinical psychology or a related field, such as psychology
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We are looking for a highly motivated postdoctoral researcher to join our lab of neuroepigenetics at EPFL (http://graefflab.epfl.ch/) ! The focus of the lab are the epigenetic underpinnings
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in Utah to recruit multiple postdoctoral fellows to apply high throughput methods and machine/deep learning to unlock the full potential of the dark proteome. Responsibilities Scientific visionRibosome
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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acquire their fate and establish precise connectivity with target brain regions during development, and how these processes are altered by stress and pathology. To address these questions, the team combines
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Postdoctoral Positions for Computational Genomics, Cancer Genetics, and Translational Cancer Biology
mechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights
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diseases, Genome Biology, 2024 S. Hudaiberdiev et al., Modeling islet enhancers using deep learning identifies candidate causal variants at loci associated with T2D and glycemic traits, PNAS, 2023 S. Li et
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senior research position to work on projects related to computational analysis of mass spectrometric datasets. A major focus will be on the application of AI/machine learning models and other computational