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these challenges by exploring and developing advanced methodologies for wind turbine drivetrain condition monitoring. The successful candidate will primarily focus on: Signal processing techniques: Developing and
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domains are e.g., signal-/image processing, artificial intelligence and machine learning. Tasks: research and development in designing and programming field programmable gate arrays (FPGAs) for accelerating
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, using techniques such as: High-dimensional data mining Tensor decomposition Causal inference Statistical process modeling Machine Learning Applications include public transport, private vehicles, traffic
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). Balancing family and career is an important issue. The positions are generally suitable for candidates seeking part-time employment. Please indicate your request in your application. The work is being done
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effects, compound hazards, and systemic interdependencies, adapting to evolving EU design codes. More specific goals are to: re-view existing hazard methods and codes for single and multiple hazards (e.g
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/learning based techniques in the areas of robotics, or autonomous systems, • interested in autonomous systems and signal processing, • Keen to work with equipment and embedded
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of hematopoietic stem cells. We are particularly interested in studying the processes and cellular signaling that lead to the emergence, maturation and expansion of stem cells in murine embryonic development and in
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Position Description The Unsteady Flow Diagnostics Laboratory (UNFoLD) led by Prof. Karen Mulleners at EPFL in Lausanne is looking for multiple PhD students to join the group in the fall of 2025 or early
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-B4 Investigators: Prof. Dr. Meng Wang, Chair of Traffic Process Automation , and co-supervised by another expert in traffic control Requirements: excellent or very good university degree
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the intersection of control theory, robotics, artificial intelligence, and signal processing, and is a key enabler of future intelligent infrastructure.This project will focus on the development of novel control