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PhD studentship: Discovery of rational therapeutic biomarkers in breast cancer by systems pathology and deep learning Supervisor: Dr Hamid Raza Ali Department/location:Cancer Research UK Cambridge
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with deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills in Python and experience with a deep learning framework such as
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, visiting researchers, master's students, etc.) Research Context Recent advances in mobile robotics have been driven by remarkable progress in perception, deep learning, and control. However, current robotic
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deep generative models (VAEs, GANs, diffusion models) or probabilistic modelling is a strong plus. You have good programming skills in Python and experience with a deep learning framework such as PyTorch
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. There is also a need for accounting noise in Deep Learning models and quantifying uncertainty in real-world applications. This PhD studentship (scholarship) leverages large-scale Earth Observation data
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) scientists, developers and advocates. This particular Doctoral Researcher (DR) will work with in-orbit results of on-going missions, such as Foresail-1 p developed by Aalto University with the E-sail payload
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Inria, the French national research institute for the digital sciences | Saclay, le de France | France | 3 months ago
) Collaborative Context and Expertise Since 2018, EDF has been studying the construction of meta-models based on deep neural networks (i.e., deep learning). Initially, based on fluid simulations using Code_Saturne
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 2 months ago
of two main parts: Improve methods for automatically aligning ontologies and linking data by leveraging the scalability, approximation, and multi-viewpoint capabilities of deep learning methods. Study
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% of your time), including tutorials and supervision of Bachelor’s theses. This is what we ask of you This is an interdisciplinary project that combines machine learning and AI, probabilistic risk
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programming skills (Python; familiarity with deep learning frameworks such as PyTorch or TensorFlow is an advantage); Affinity with probabilistic modelling and spatial data analysis; Interest in the physical