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solutions and applying them in real-world scenarios. Proficiency with machine learning frameworks and pipelines in SKLearn, Numpy, Pandas, and PyTorch. Proficiency with deep learning frameworks such as
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National University of Science and Technology POLITEHNICA Bucharest, Pitesti Branch | Romania | about 1 month ago
(machine learning, deep learning); adaptive control and algorithmic optimization; integration of AI models in embedded systems and software platforms. APPLICATION Before applying, all candidates are invited
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ACBs have successfully pursued a variety of subsequent career directions including obtaining MDs, PhDs, and MD-PhDs on the paths to careers in both academia and industry. The ideal candidate will have
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of the host organization of last 36 months. As secondments and events are foreseen, applicants must be ready to travel Applicants must be eligible to enroll on a PhD program at TU Dresden (see https://tu
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) Experience in deep learning algorithms is a plus Ability to work in a highly international team and interdisciplinary project applicants are expected to have excellent language skills in English Opportunity
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Doctoral Candidate in computer vision and machine learning for developing novel deep learning method
Machine Learning (DM3L) Doctoral Candidate in computer vision and machine learning for developing novel deep learning methods for satellite-based tracking of global CO2 and NOX emissions of point sources 80
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with the centre’s user partner Kongsberg Satellite Services (KSAT). We are therefore seeking someone with a strong interest and competence in deep learning. Working environment: The project will be done
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and development of perception stacks for autonomous mobile systems in general in any field Machine learning/deep learning experience applied to perception and any experience with deep Learning
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using deep learning or causal learning methods. Candidates must have solid experience with large spatial and temporal datasets, large model manipulation, and HPC. The candidate must also have experience
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https://pubmed.ncbi.nlm.nih.gov/36596869/ Research area: Cancer biology Keywords: lymphoma, CLL, lncRNA, microenvironment Funding of the PhD candidate: Part-time salary (min. 0,5 FTE) on EHA grant/AZV