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. Researching and developing novel machine learning architectures for integration across multiple types of high-dimensional data. Researching and implementing novel algorithms for analysis of latent factors and
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Qualifications* PhD in Computer Science or Engineering, Biomedical Engineering, Neuroscience, Bioinformatics, or other relevant field. Experience with machine learning and statistical analyses. Proficiency with
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models (e.g., YOLO, U-Net, EfficientNet, ResNet, FPN, Fast R-CNN) Computer vision techniques and algorithms Python and relevant libraries (e.g., PyQt, OpenCV, NumPy, scikit-learn), particularly
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emerging technologies such as machine learning and artificial intelligence. In addition, the ideal applicant will have excellent communication skills and have demonstrated capacity and aptitude for effective
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organ-on-a-chip (OOC) models, colony picking and bioprinting). The ideal candidate should have strong expertise performing machine learning (ML), computational biology with the capability and/or
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in the position and outline skills and experience that directly relate to this position. Job Summary The candidate will lead projects in building machine learning models to screen potential drugs
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collaborative relationships to ensure that the overall project is progressing together and on schedule. Developing and maintaining machine learning software infrastructure for experiments, and coordinating
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machine learning software infrastructure for experiments, and coordinating software development across the team. Ensuring electrical safety of the experimental equipment and regulatory compliance
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/Statistics, Medical/Health Informatics. Strong computational and programming skills with abilities to develop cutting-edge large-scale machine/deep learning algorithms using high-performance computing (HPC
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biology and genomics, with a special emphasis on genomics - in statistical data analysis and data visualization methods with a special emphasis on genomic data - with machine learning and artificial