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This is a unified application form for all positions in the Beyesian Deep Learning group at KAUST led by Prof Maurizio Filippone, including Research Intern MS/PhD Student PhD Student Postdoctoral
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Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 23 hours ago
Job Requirements REQUIRED: PhD in a field of quantitative science; strong background in machine learning and signal processing; experience with and commitment to rigorous and reproducible
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to validate the tools against practical design requirements. The successful Machine Learning Engineer will have the following skills, experience and attributes: MSc/MEng or PhD (desirable) in Machine Learning
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. This will allow for a deep dive into the synchronic and diachronic properties of predictability. The PhD candidate will have the opportunity to collaborate with members of the project involved in developing
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. This role offers a unique opportunity to work at the cutting edge of deep tech nano-biotechnology, while actively participating in real-world commercialization pathways and venture design. Strong performance
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Supervise and mentor research associates, engineers, and PhD students working on the project Collaborate with perception, controls, systems, and hardware teams to guide integration of learned policies
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to validate the tools against practical design requirements. The successful Machine Learning Engineer will have the following skills, experience and attributes: MSc/MEng or PhD (desirable) in Machine Learning
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at least one mainstream deep learning framework (e.g., PyTorch, JAX) • Expertise in (atomistic) thermodynamic, kinetic simulations or computational chemistry • Ability to independently design and
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This PhD research aims to develop and evaluate explainable AI and machine-learning approaches for early detection, risk stratification and prognostic prediction of colorectal cancer, with a focus on
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imaging. The successful candidate will therefore be working primarily as an AI/image-processing researcher, developing deep-learning models for medical images.