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Postdoctoral Research Associate will be responsible for AI-driven materials discovery. The position will collaborate with interdisciplinary teams to develop integrated molecular simulation and machine learning
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it comes to running experiments an affinity to AI technology (incl. machine learning, computer vision) (German) permit to perform animal experiments (equivalent to FELASA B or C) desirable previous
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for testing machine learning algorithms applied to generative design in support of existing heritage regeneration processes. Where to apply E-mail reclutamento.docenti@ateneo.uniroma3.it Requirements Additional
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the Institute of Psychiatry, Psychology, and Neuroscience (IoPPN) in KCL. The candidate is required to: Have a PhD in Signal Processing, Machine Learning, or Bioengineering from a reputed university. Have a
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, electrical engineering, etc. Prior experience in (1) image processing, particularly for radiographic and computed tomographic data as well as mesh-type data, and (2) machine learning, particularly deep
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-centered learning as well as how students from backgrounds traditionally considered “outside” the academy experience and engage in STEM learning and career training. The postdoctoral associate will
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that integrate multi-omics data to uncover mechanisms of disease, cellular resilience, and therapeutic response. The post holder will lead research applying large-scale machine learning and foundation models
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neurological departments of Lyon's hospitals. Lyon is a vibrant and beautiful city, just 1 hour from the French Alps by car, and 2 hours from the Mediterranean coast and from Paris by train. More on our work can
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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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learning. Job responsibilities will include: Develop simulation algorithms and software to model challenging gas adsorption behavior in porous materials Develop novel machine learning model for predicting