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Field
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explore and develop new types of memristors that can be switched both optically and electronically, therefore opening the route to new applications in computer vision. The position will be based in
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: Experience in Computer Vision and machine learning. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding valuation: the first phase comprises the Academic Evaluation (AC
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benefit from health insurance, supported by INESC TEC. 2. OBJECTIVES: ● Research and develop novel reliable deep learning computer vision algorithms for the detection and quantification of GIM lesions
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. Citizen Only Degree: Doctoral Degree. Discipline(s): Chemistry and Materials Sciences (1 ) Computer, Information, and Data Sciences (1 ) Life Health and Medical Sciences (5 ) ORISE GO The ORISE GO
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: Essential criteria A doctorate (or will shortly satisfy the requirements of a PhD). The doctorate must be in a relevant discipline area, such as statistical machine learning, computational and quantitative
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phenotypes in warm water aquaculture species. The fellow will learn to utilize genome-scale datasets from host-pathogen interaction studies along with high-performance computing resources to answer scientific
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Requirements Academic qualifications: -. Minimum profile: Experience in Computer Vision and machine learning. Preference factors: Experience in research projects, and writing of scientific papers. Research
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, and writing of scientific papers. Minimum requirements: Experience in Computer Vision and machine learning. 5. EVALUATION OF APPLICATIONS AND SELECTION PROCESS: Selection criteria and corresponding
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(e.g., Docker, Kubernetes, cloud/edge environments). Demonstrated expertise in AI, distributed computing, machine learning, or systems software design. Strong background in software engineering
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equivalent. Strong background in machine learning and computer vision. Prior experience in data-efficient classification, synthesis, and detection is preferable. Strong publication records in top-tier machine