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teaming for exploration. Candidates interested are encouraged to visit the ESA website: http://www.esa.int Field(s) of activity for the internship Topic of the internship: Optimisation for Robotic Mobility
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working under the supervision of Prof. Jaideep Vaidya (the PI and Director, I-DSLA) to develop and analyze privacy-preserving solutions for biomedical data research, implementing the developed algorithms
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team for the achievement of the project objectives. Where to apply Website https://karjera.ktu.edu/en-GB/jobs/6964131-research-project-noninvasive-monitor… Requirements Research FieldEngineeringEducation
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stress recognition algorithms; A collection of physiological signal data for stress recognition that can be reused in other scientific research. Expected technological results: A technological solution for
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algorithmic innovation and robot deployment, leading experimental design, system integration, and evaluation on real robotic platforms. The Embodied AI and Robotics Lab (AIR) develops intelligent robotic
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of hyperbolic deep learning and one PhD student with a keen interest in the algorithmic side of hyperbolic deep learning. Tasks and responsibilities: Conduct high-impact research on hyperbolic deep learning
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models. Knowledge base of multivariable and vector calculus, as well as geometric algebra. Familiarity with machine learning and algorithms. Knowledge and proficiency with protein Large Language Models
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of Opportunity (SOP) for geolocation. The laboratory has developed algorithms to perform geolocation using such signals. We now wish to move on to an experimental phase. The mission will take place at the SAMOVAR
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in our department. For more information on our department, please see our website: https://med.stanford.edu/anesthesia.html Duties include: Collect, manage and clean datasets. Employ new and existing
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well as 5+ years of financial industry experience developing quantitative trading models and algorithms, particularly in crypto markets and cryptocurrencies. For more information about the QFC program in MIDS