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for remote sensing and uncertainty estimation. Candidates must have a strong programming background. Requirements: PhD in Computer Science or a related field with a strong emphasis on machine learning
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exam before 15.06.2026. It is a condition of employment that the master's degree has been awarded. Background in optimization is required. Experience in machine learning is an advantage. Familiarity with
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, causal inference, and machine learning to study real-world treatment patterns, effectiveness, and safety of monoclonal antibodies (mAbs) used in autoimmune, inflammatory, and neurological diseases. Using
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% of the fellowship time to personal research. This is a one-year fellowship appointment, with the possibility of renewal for two additional years. Applicants must have fulfilled all the requirements for the PhD by
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relevant areas (e.g., software engineering, cybersecurity, program analysis, machine learning), as evidenced by transcripts. Relevant professional or research experience in software security, static analysis
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. To perform any other duties related to the research program. Job Requirements: Preferably PhD in Computer Engineering, Computer Science, Electronics Engineering or equivalent. Independent, highly analytical
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hands-on experience in areas such as computer vision, deep learning, multi-modal sensing, robotics, structural health monitoring, or digital twin technologies. (Fresh PhD graduates are welcome
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the researchers from Department of Automation and Process Engineering will play a key role. We welcome motivated applicants in robotics, control, AI, machine learning, physics, and related fields, including early
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engineering or related field At least 1 year of relevant experience in signal processing and machine learning. Good written and oral communication skills Proficiency in ANSYS, and lab test skill Ability to work
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perform 3D single-particle tracking and establish pipelines to characterise the particle motion using a combination of established tracking algorithms and machine-learning-based approaches. Additionally