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Field
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international reputation for creating high impact data visualization tools and we are driven to solve the most challenging design and engineering problems where biomedical data, humans, and AI meet. We would
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in mechanical engineering, materials science, computational engineering, manufacturing engineering, or a related field. Have a strong background in CFD and multiphysics modelling, including experience
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Postdoc in AI-driven design of environment-responsive antibodies and nanobodies - DTU Bioengineering
this project, you will work at the interface of machine learning and protein engineering, computationally designing antibodies, nanobodies, and binding proteins with condition-dependent activity. You
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University of Massachusetts Chan Medical School | Worcester, Massachusetts | United States | 3 months ago
. REQUIRED QUALIFICATIONS: •Ph.D. (or imminent defense) in Computational Biology, Bioinformatics, Computer Science, Applied Mathematics, Statistics, Biophysics, Biomedical Engineering, or a related field
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, Computational Biology, Computer Science, Biomedical Engineering, or a related quantitative discipline Strong background in machine learning/deep learning (e.g., CNNs, transformers, vision models
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, Electrical/Computer Engineering, Bioinformatics, Computational Biology, Data Science, Applied Mathematics, Statistics, or a related quantitative discipline. Strong programming skills in Python
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Details Title Research Associate and Postdoctoral Fellowships in Cardiac Cell and Tissue Engineering School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Disease
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genetics, computational biology, engineering, and therapeutic development, with the aim of creating transformative programs in human neuroscience and neurological disease. For the coming year, we are
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, developing expertise in AI methodology, biological data analysis, software engineering, and computational modelling. The research could contribute to more efficient and sustainable production of medicines
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research will be conducted at Cranfield University within a supportive, cross-disciplinary environment spanning energy systems engineering, computational modelling, bioinformatics and food safety