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We are seeking an enthusiastic and self-motivated teacher to join the Department of Computer Science as a Lecturer/Senior Lecturer in Computer Engineering/Hardware. This role offers an exciting
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in privacy preserving machine learning (ML) within the SSF-ML-DH project, under the supervision of Olivier Cappé (CNRS, DI ENS) and Jamal Atif (Ecole Polytechnique, CMAP). Funding is available for two
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or computational protein design. In particular, you will have: A PhD, or near completion of a PhD, in machine learning, structural bioinformatics, computational biology, computer science or a closely related
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potential at work, learn more about our employee value proposition here (You Belong Here ). The Division of Academic and Student Affairs is the place to work for values-driven faculty and staff who
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student-centred learning improvements. Experience developing and leading high-quality research projects, attracting external research funding, supervising higher degree by research candidates, and building
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About the Opportunity Conduct research on machine learning, control theory, and synthetic biology. The work will combine tools from dynamical systems, control theory, and the theory of algorithms
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IOCB Prague (Institute of Organic Chemistry and Biochemistry of the Czech Academy of Sciences) | Czech | 2 months ago
bioinformatics or molecular biology position. We're looking for someone genuinely excited by the intersection of structural/molecular biology, physics-based simulation, and machine learning/deep learning – a
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, Physics, Electrical Engineering, Communication Engineering, or equivalents; – Knowledge in artificial intelligence, statistical and machine learning, complex systems, agent-based modeling and simulation
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Associação do Instituto Superior Técnico para a Investigação e Desenvolvimento _IST-ID | Portugal | 3 months ago
- SIPG LAB/ISR and 1801P.01460.1.04 LARSYS/ISR BASE 2025-2029 – SIPG LAB/ISR, financed by national funds through FCT/MCTES Workplan: Develop new representations for computer vision and machine learning
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process data locally while ensuring efficient and scalable artificial intelligence at the edge. TinyML and Edge AI have demonstrated the feasibility of embedding machine learning models on such devices