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Field
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researcher in natural language processing and large language models to work with a team from multiple disciplines of machine learning and artificial intelligence to develop multimodal large language models
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(with the potential to be extended) with the Alabama Center for the Advancement of Artificial Intelligence. Leads the forefront of research in artificial intelligence, machine learning, and data science
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Faculty of Life Sciences and Medicine Hub for Applied Bioinformatics). We are looking for an ambitious candidate with established expertise in bioinformatics, specifically dealing with large data sets and
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computational materials science techniques (DFT, MD, machine learning force fields) with data-driven approaches. Design and implement high-throughput experimental workflows for thermal conductivity and phonon
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population genetics, bioinformatics, computational biology, statistics or probabilistic machine learning and computer science. Experience of working with large genotyping or sequencing data sets A proven
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Desirable criteria Experience of advanced statistical and/or machine learning methods, such as longitudinal analysis methods, latent variables models, clustering algorithms, missing data and clinical trial
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results. Preferred Qualifications: Experience with generative AI deep learning and active involvement in data science and machine learning projects. Experience in neural network architecture, cloud
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Responsibilities: Integrate and analyze large-scale multi-omics datasets (genomics, transcriptomics, epigenomics) to derive biological insights Apply statistical and machine learning models to identify cancer risk
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++, or Go, and frameworks like PyTorch or TensorFlow, is highly advantageous. Experience in developing and deploying machine learning models, particularly in natural language processing (NLP) and large
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illness. We have a large team working on developing technological solutions for these applications. We are seeking a computer science researcher to take an active role in developing novel machine learning