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one year, renewable Appointment Start Date: Sept-Oct 2025 Group or Departmental Website: http://cegelskilab.stanford.edu (link is external) How to Submit Application Materials: Please email the PI
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with a range of stakeholders as well as the ability to pro-actively drive the projects’ work. They will need to provide leadership and show initiative within a dynamic team environment to ensure
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demonstrated contributions to publications in top tier conferences and journals in data mining/machine learning, Artificial Intelligence in Education and Educational Data Mining, Human-Computer Interaction
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communication skills in English and good knowledge of French or German Ability to work both independently and as part of a collaborative team Ability to meet deadlines and demonstrate methodological, organized
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microinjections and confocal and live-cell microscopy techniques is advantageous Good communication and time management skills are vital Ability to conduct research independently combined with a strong ability to
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is complete. Incomplete applications and applications sent by email will not be considered. Contact details to references will be requested after the interview. Application deadline: 10 October, 2025
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field of study. Experience in computational fluid dynamics or thermal hydraulic simulation. Proven scientific research ability evidenced through peer-reviewed publications and/or presentations
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focuses on developing a cutting-edge hybrid quantum computing platform that integrates entangled superconducting qubits with single electron-on-neon (eNe) qubits. Key Responsibilities: Design and fabricate
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degree in the area of power engineering, power electronics, controls, or energy systems Required Experience Experience performing research assistance activities during the doctoral program Preferred
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Max Planck Institute for Brain Research, Frankfurt am Main | Frankfurt am Main, Hessen | Germany | about 1 month ago
electrophysiology, functional imaging, or light/electron microscopy Familiarity with genetic and molecular biology tools Background in computational modeling of neural systems A demonstrated ability to secure