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reconfigurable RF hardware for CAP-MIMO systems and contributing to machine learning-enhanced ISAC methods development through EM-informed modelling and hardware design. This is a unique opportunity to build
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master’s degree with academic qualifications in digital health, data analysis, and/or machine learning applied to health research. Admission to the PhD program requires a 120 ECTS master’s degree, including
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growth methodology based on real-time growth monitoring enabled by advanced in situ characterization tools (RHEED, ellipsometry, curvature measurements, flux monitoring), coupled with machine-learning (ML
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for a/an University assistant predoctoral - PhD Position in Graph Learning 39 Faculty of Computer Science Startdate: 01.05.2026 | Working hours: 30 | Collective bargaining agreement: §48 VwGr. B1
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Job Requirement Have relevant competence in the areas of Deep Learning/Computer Vision. The experience in diffusion models is a plus. Have a PhD degree in computer science/engineering or related
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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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for machine-brain interface, tissue repair, and disease diagnosis, monitoring and treatment, are especially encouraged to apply. The appointee is expected to conduct world-class research and to teach
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analysis and/or advanced algebra or algebraic topology. Knowledge and experience of machine learning. Personal characteristics To complete a doctoral degree (PhD), it is important that you are able to: Work
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an increased interest in adapting and developing the latest machine learning methods for the purpose of malware detection, and preliminary results are encouraging. The specific goals of this project include
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data (HRMS) used for non-target analysis. The projects aims to develop a combination of supervised and unsupervise machine learning stragaties for pinpointing chemicals that have high toxicity