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the Interpretable Machine Learning Lab (https://users.cs.duke.edu/~cynthia/home.html ) for a scientific developer to work in collaboration with other researchers on machine learning tools that help humans make better
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for this position will be a highly motivated individual with experience in deep learning and medical imaging and a PhD degree in computer science, electrical and computer engineering, biomedical engineering
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. Minimum Requirements: The position requires a PhD in biological sciences, chemistry, or a related discipline and a strong record of publications in peer-reviewed journals. Preferred Qualifications: Ideal
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-performance computing. Additional experience in DNA biophysics, machine learning, and optimization is preferred. This job description intends to provide a representative and level of the types of duties and
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will develop novel statistical and machine learning methods for any of the following: multi-omics data (such as bulk and large-scale single-cell RNA sequencing data, spatial transcriptomics, bulk and
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utilize new machine learning methods for 3D behavior tracking and analysis. · Advise PhD students on related projects. Other Work Performed and Expectations · Document progress consistently and
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, including development of new computational tools for processing large-scale biospecimen data Creation of novel machine learning frameworks for automated scientific analysis and discovery Design and
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analysis and machine learning methods applied to protein structure determination using single-particle cryo-electron tomography (ET). The candidate will contribute to the design, development, and
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theory, multi-objective optimization and machine learning. The specific project aims to understand the multiscale interactions shaping human gut bacteria and human gut pathogens. The project will combine
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, ChIP-seq, and ATAC-seq, CRISPR and RNAi perturbation screens 3. Ability to build predictive statistical and machine learning models that integrate multiple data types, including linear and nonlinear ML