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this position, you will ideally bring the following: For the Networking and AI position: Completed a PhD in Computer Science, Electrical & Computer Engineering, or a closely related field Expert knowledge of deep
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funds, facilities and resources which includes things like PhD scholarships, seed funding and research spaces. Collaborative approach: Engage with staff and HDR candidates across all relevant teams
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position at the global vanguard of data science and artificial intelligence. The successful candidate will lead a distinguished faculty and a vibrant community of researchers dedicated to bridging deep
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. Describe a deep learning project you have executed, ideally a creative use of supervised fine tuning of a pre-trained vision transformer, U-Net architecture, or related topic. Projects in computer vision for
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Leibniz-Institute for Food Systems Biology at the Technical University of Munich | Freising, Bayern | Germany | 2 months ago
well as experience in omics data analysis, and possesses solid English-language skills. Experience with programming, preferably Python and R, is required. Experience with deep learning frameworks, such as JAX
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Term: Initially 1 year, renewable. Appointment Start Date: As early as February 2026, but flexible Group or Departmental Website: https://med.stanford.edu/bridge-lab.html (link is external) How to Submit
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understanding of advanced programming areas, including modern machine learning and deep learning methods (e.g., MATLAB and Python) Expertise in one or more of the following areas is preferred: mechanical
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focuses on single-cell genomics, biotechnology, and bioinformatics. The project involves transcriptomic and genomic profiling of single microbes. The post-doc will work on machine and deep learning methods
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software development. E3 Experience in training deep learning models relevant in research projects at scale. E4 Experience of applying good software engineering practices including but not limited
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courses in computing and related areas, with preference for candidates who can teach in one of the following areas: AI and Machine learning (courses like Applied Machine Learning, Deep Learning