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simulation environments, numerical methods, or machine learning approaches is an advantage Fluent command of written and spoken English is necessary; German is an advantage but not required High degree
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into real-world applications in Geriatronics. Your Profile PhD in Robotics, Mechanical Engineering, Electrical Engineering, Computer Engineering, Mechatronics, or a closely related field. Proven experience in
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-based statistical signal processing techniques and data-driven machine learning methods. The Signal Processing Division has access to excellent high-performance computing facilities, measurement equipment
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Karlsruher Institut für Technologie (KIT) | Karlsruhe, Baden W rttemberg | Germany | about 2 months ago
of the developed algorithms) Empirical investigation of use cases Scientific exchange and publication of results This position offers the possibility to acquire a PhD degree, which is encouraged. This research
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equivalent university degree in psychology or a related discipline (computer science, neurosciences, biology, cognitive sciences) • Skills in perception psychology, neurosciences, machine learning, tracking
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protein structure analysis. Required skills: High motivation, curiosity, self-driven, critical thinking, strong team-player, good English, high interest in protein structure and machine learning. This PhD
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Requirements: Applicants should hold an MSc or Diploma in Engineering, Computer Science or a related discipline. Background in Machine Learning and Artificial Intelligence. Strong programming skills (Python
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and others) Analysis of the experimental data, ideally connecting to our machine learning tools Presentation of scientific results on conferences and in publications Requirements PhD degree in physics
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Menopausal Women” with full-time employment for a duration of 3 years, starting in February 2026. Objective of the project: BrainAGE is a machine learning-based biomarker that estimates biological brain age
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elements distribution, crystallographic texture), mechanical properties (hardness, yield and tensile strength) and corrosion profile (rate and localization). This work focuses on machine learning assisted