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Field
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using LLMs in therapeutic settings: data confidentiality, algorithmic biases, and limitations in contextual understanding. Study the acceptance of these tools by both patients and healthcare professionals
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on the development of algorithms for ECG signal analysis and validation of novel biodegradable ECG electrodes. The contract will be within the framework of the “Green Electrodes for Sustainable Electrophysiology
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Engineering, Databases, Computer Networks, Computer Vision and Graphics, Algorithms, and Computational Biology. Salary and Conditions of Service Remuneration package will be driven by market competitiveness
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develops solutions for a range of vision tasks via machine learning and deep learning algorithms. The SSUDIO project aims to identify various objects of interest from shipboard 3D scans by training computer
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emphasis on questions grounded in data that are generated by human activity, including computational social science (e.g., algorithmic accountability and the interplay of data science with policy, law, and
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for fast tune-up Implementation and benchmarking of quantum algorithms Qualifications We are seeking candidates with: A PhD in Physics, Applied Physics, Nanotechnology, Computer Science, Engineering, or a
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algorithms are agnostic of the downstream task they will be deployed on, and this may lead to a suboptimal control performance. In this project, we will investigate control-oriented biases and their impact on
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datasets Implement LLM and Machine Learning algorithm Conduct statistical analysis in SAS or Stata Assistant with other ad hoc tasks Required Education Bachelor’s or Master’s degree in computer science
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analysis Theoretical analysis of neural networks and deep learning Foundations of reinforcement learning and bandit algorithms Mathematical and algorithmic perspectives on large language models Statistical
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positions in theoretical computer science, algorithms, and complexity. Founded in 2022, INSAIT’s mission is to establish itself as a world-class research institution and has attracted outstanding faculty