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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 20 hours ago
place to teach, work and learn. One of the best college towns and best places to live in the United States, Chapel Hill has diverse social, cultural, recreation and professional opportunities that span
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and guide their projects in collaboration with other post-doctoral researchers and the group leaders. At Adelaide University, we create the opportunities you need to achieve your ambitions – because
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, and conservation biology, collaborating with world-class researchers while working with some of the largest genomic datasets (e.g. Vertebrate Genomes Project, California Genomes Project) ever assembled
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programming and instrument control using Matlab, Python, Labview etc Machine / deep learning expertise Strong analytical skills and ability to work in a multidisciplinary team Excellent communication and
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental
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discipline Strong experience in integrating several of the following components: Deep learning and LLMs for molecular biology Vision foundation models for pathological image analysis Multi-omics datasets (e.g
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focused on exploration and development of AI models of auditory perception, towards a broader goal of understanding how the brain predicts and learns from human communication sounds such as speech and music
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candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
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highly multidisciplinary environment, collaborating closely with radiologists, software engineers, and data scientists to advance clinically relevant AI methods. Your research is expected to lead to high
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. Weinstein (DTU Chemistry) to develop novel molecular ML methods and use them to control, steer, and learn from large scale wetlab experiments. You will deploy these methods in collaboration with Prof. Katrine