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, applied mathematics, or a closely related discipline, with previous experience in statistical and machine learning methods and knowledge in basic biology. Applicants close to completion of their PhDs will
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processing, and machine learning techniques. Excellent communication and written skills. Required Application Materials CV and cover letter Website me.msu.edu MSU Statement Michigan State University has been
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the intersection of machine learning (ML) and the sounds of wildlife (“bioacoustics”). We are also happy to consider candidates in one of the two fields who can demonstrate a strong basis for working in this cross
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). The researcher should have a PhD/DPhil in robotics, computer vision, machine learning or a closely related field. You have an excellent academic track record in topics relevant to robot perception. A specific
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modelling, and machine learning approaches to analyse large-scale datasets, including bulk and single-cell sequencing, gene expression arrays, proteomics, and metabolomics. Working closely with senior
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interested in mentoring and supporting MSc and PhD students. You are a machine learning enthusiast (and realist). You love coding and have proven experience in e.g. Python, Matlab, JAVA, C#. You can present
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machine learning within the scope of ACAG. The postdoc will have various opportunities for professional development, including contributing to and leading grant proposal development (e.g., external
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Post-Doctoral Associate in the Division of Engineering (Mechanical Engineering) - Dr. Mohammed Daqaq
and experimental, in the broad field of nonlinear mechanics. Preference will be given to applicants with expertise in machine learning, wave propagation, metamaterials, and/or fluid–structure
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are especially interested in individuals with both philosophical background and an understanding of recent machine learning technologies to work on topics related to explainability, interpretability, and/or
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(or similar): Coherent diffractive imaging, especially ptychography. Sparse sensing, optimization, or Bayesian experimental design. Machine learning for imaging. Synchrotron experiment experience. Semiconductor