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search strategies and lexicons. Proficiency in fitting and validating statistical models or machine learning algorithms is essential, along with advanced skills in R and/or Python for data processing and
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learning-based computer vision algorithms and software for object detection, classification, and segmentation. Key Responsibilities Participate in and manage the research project together with the PI, Co-PI
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11 Feb 2026 Job Information Organisation/Company INESC TEC Research Field Computer science » Computer systems Researcher Profile First Stage Researcher (R1) Application Deadline 25 Feb 2026 - 23:59
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Research Fellow in AI Methods for Creative Applications Apply for this job See advertisement About the position Position as Postdoctoral Research Fellow available at Department of Informatics, University
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, generative AI, NLP, or algorithmic decision systems Ideal applicants will have a strong background in operations research, statistics, or computer sciences and the ability to work across disciplinary
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algorithms especially in relation to multimodal signals, physiological computing, emotion recognition, and biofeedback experience in music technology and/or sound and music computing experience with user
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the research project - “Large language models for computational social science and FinTech applications”. He/She will be required to: (a) study and investigate the fundamental problem for identifying
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to the applications of mathematics in cryptography, computing, business, and finance. PAP covers many areas of fundamental and applied physics, including quantum information, condensed matter physics, biophysics, and
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Job Description Job Alerts Link Apply now Job Title: Research Fellow (High-Performance AI Computing Systems) Posting Start Date: 20/01/2026 Job Description: Job Description We are seeking a highly
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AUSTRALIAN NATIONAL UNIVERSITY (ANU) | Canberra, Australian Capital Territory | Australia | 23 days ago
/lanfear-lab-mutation-molecular-evolution-and-phylogenetics The Person You will need to have a detailed understanding of cutting-edge methods and algorithms in maximum-likelihood phylogenetic inference, as