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Field
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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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SD-26086 POSTDOCTORAL RESEARCHER IN THE PHYSICAL VAPOR DEPOSITION OF ELECTROCATALYST FOR PHOTOANO...
a deep understanding on the electrochemical behaviour of the produced thin films and develop strategies to enhance their functional properties. The postdoctoral researcher, integrated to a very
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SD-26083 – POSTDOCTORAL RESEARCHER IN THE CHEMICAL VAPOR DEPOSITION OF METAL ORGANIC FRAMEWORKS F...
with members of the hosting group to build up a deep understanding on the electrochemical behaviour of the produced thin films and develop strategies to enhance their functional properties
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SD-26084 – POSTDOCTORAL RESEARCHER IN THE CHEMICAL VAPOR DEPOSITION OF COVALENT ORGANIC FRAMEWORK...
with members of the hosting group to build up a deep understanding on the electrochemical behaviour of the produced thin films and develop strategies to enhance their functional properties
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SD-26085 POSTDOCTORAL RESEARCHER IN THE OXIDATIVE CHEMICAL VAPOR DEPOSITION OF FUNCTIONAL BUILDIN...
to build up a deep understanding on the electrochemical behaviour of the produced thin films and develop strategies to enhance their functional properties. The postdoctoral researcher, integrated to a very
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implications for GGR and SRM deployment. (3) Identifying social acceptance and legitimacy, (4) Learning, diffusion and adoption in GGR and SRM technologies, (5) Implications for Sustainable Development Goals
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. Candidates whose doctoral work focused on deep learning methods and who have a strong interest in genomics will also be considered. Experience: At least one publication in computational genomics or machine
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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, tape-out, and testing, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing. ● Emerging AI
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data (PET, CT, Magnetic Resonance Imaging with Late Gadolinium Enhancement – MRI-LGE) and clinical variables. The approach encompasses unsupervised multimodal registration, three-dimensional deep