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Field
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applying machine learning and computational methods for protein design, in close integration with experimental enzymology and biocatalysis. The tasks include: Development and application of AI and machine
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, Imperial College London, Ashoka University, the Communicable Diseases Agency Singapore (CDA), the National Environment Agency Singapore (NEA), the Machine Learning & Global Health Network (MLGH), and wider
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related field At least 1 year of relevant experience in signal processing and machine learning. Good written and oral communication skills Proficiency in ANSYS, and lab test skill Ability to work
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modeling, satellite remote sensing, or machine learning Programming skills (Python or R) Strong publication record
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Requirements: Ph.D. in Electrical and Electronic Engineering, Computer Engineering, Computer Science, Artificial Intelligence, Machine Learning, Human-Computer Interaction, Biomedical Engineering, or other
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 1 month ago
independently; and ability to work as part of a tightly-knit team. PREFERRED: Experience with theoretical analysis, using and building machine learning models, and developing circuit models. 3/16/2026
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as possible thereafter. The Role Drawing on over a decade of formulation data, you will develop machine learning models that link chemical composition and processing parameters to the physical and
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track record in neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches
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user and stakeholder engagement. The candidate will be embedded in a multidisciplinary research environment combining expertise in machine learning (ML), numerical modelling, satellite remote sensing
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neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches). * Demonstrated track record in multiscale