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APPLICATION INSTRUCTIONS: CURRENT PENN STATE EMPLOYEE (faculty, staff, technical service, or student), please login to Workday to complete the internal application process . Please do not apply
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habitual control. It will then test how acute stress alters these processes, combining behavioral performance, physiological monitoring, movement-based phenotyping, and advanced statistical and computational
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graphics, machine learning and graphics programming, as well as a good command of English. Knowledge of data science and deep learning is an advantage. You demonstrate good team spirit, interpersonal and
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.; An asymmetric nautilus-like HflK/C assembly controls FtsH proteolysis of membrane proteins, The EMBO Journal, 44, 2501 (2025). 4. Ghanbarpour, A.; Sauer, R. T; Davis, J. H.; A proteolytic AAA+ machine poised
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, formal methods, convex geometry). 2. Master’s or PhD in Computer Science, Electrical Engineering, Control Systems, or a closely related field. 3. Experience in reachability analysis, formal verification
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that drastically reduces power and latency. We are looking for a Postdoctoral Researcher/Research Engineer with a background in Embedded Systems and Low-Level Programming to design the control logic that powers
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or preferably more topics from these areas is considered a merit. - 5G reconfigurable Connectivity - 5G/6G QoS - Control of Communication networks - Markovian Processes - Network Based Localisation / Radio based
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. In this role, you will leverage large-scale, heterogeneous datasets to develop and deploy AI-driven methods for: Real-time quality monitoring and control of manufacturing processes Understanding
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) for electromechanical systems Microfabrication of MEMS devices using cleanroom processes Experimental testing and characterization of MEMS resonators and frequency control devices Electronic circuit integration and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 13 days ago
inform representations and encoder adaptations. This activity will provide hands-on experience in making three-dimensional vegetation structure accessible within data-driven geospatial modeling processes