18 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at Empa
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statistical evaluation Machine learning analyses: implementation of established and new workflows Coordination of activities with Consortium partners, including presentation of results at consortium meetings
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profile PhD in Computer Science, Data Science, Machine Learning, or a related discipline. Proven experience in computer vision (e.g. image processing, deep learning, object detection, segmentation) and
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, presentations and scientific publications Your profile You have a PhD degree (or equivalent) in Acoustics or Electrical/Mechanical Engineering or Computer Science. Experience with scientific computing in Matlab
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having a high degree of independence. Your profile A PhD degree in Civil Engineering, Materials Science, Chemistry, or a related field, as well as strong management and communication skills are required
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industrial partner, you will design and implement innovative architectures for real-time detection and control of laser processes. This interdisciplinary role combines artificial intelligence and machine
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Application Deadline 25 Nov 2025 - 22:59 (UTC) Type of Contract Permanent Job Status Full-time Hours Per Week 38 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is
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) Country Switzerland Application Deadline 1 Jan 2026 - 22:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 38 Is the job funded through the EU Research Framework Programme? Not funded
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. Supervise Bachelor’s and Master’s theses within the scope of the project. Contribute to the preparation of research proposals and technical reports. Your profile PhD in civil/structural engineering (or
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Deadline 19 Nov 2025 - 22:59 (UTC) Type of Contract Permanent Job Status Full-time Hours Per Week 38 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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of our Materials Vision Tech initiative, we focus on multi-element gradient thin film systems, i.e. their rapid deposition and automated multi-technique characterization. Within the Swiss-Polish innovation