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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 1 month ago
is available here: https://acrobat.adobe.com/id/urn:aaid:sc:VA6C2:b74beba8-716f-4ab4-b791-2fb6c087ca7f Job Requirements REQUIRED: Ph.D. in neuroscience, biomedical engineering, chemical engineering
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subsea digital twin of deep-water mooring lines for floating offshore wind turbines. The digital twin will be integrated with machine learning algorithms for detection of primary entanglement due
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machine learning. The employment This employment is a temporary contract of two years with the possibility of extension up to a total maximum of three years. The employment is full-time. Starting date by
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requirements. Required Qualifications Ph.D. in Electrical and Computer Engineering, Computer Science Engineering, Biomedical Engineering, Integrated Systems Engineering, Mathematics, Statistics, or equivalent
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broad spectrum of fields, from core to applied computer sciences. Its vast scope also benefits our undergraduate and graduate programmes, and we now teach courses in several engineering programmes
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metaproteomics approaches Analyzing large-scale multi-omics and clinical datasets to investigate individual metabolic responses to diet. The work includes applying advanced statistical and machine learning methods
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, including exoplanet studies, machine learning, cutting-edge radio instrumentation and digital signal processing, citizen science, sky surveys, and studies of transient and variable objects. Listen is deeply
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). - Familiarity with machine learning principles and generative/classification models (PyTorch Lightning, torch, scikit-learn, etc.), as well as data/model analysis methods (PCA, t-SNE, etc.). - Proficiency in
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. Essential qualifications and experience a PhD (or near completion) in one of the following fields (or a closely related discipline): Computer Science, Artificial Intelligence, or Machine Learning Economics or
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psychoactive substances, in seized drug products or clinical samples. The candidate will have the opportunity to work directly with experimentalists to validate predictions made by their machine-learning models