26 phd-studenship-in-computer-vision-and-machine-learning Postdoctoral positions at UNIVERSITY OF HELSINKI in Finland
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machine learning. We focus on inductive logic programming (ILP), which learns logical rules from data. We primarily use automated reasoning techniques, such as SAT/ASP/SMT/MaxSAT solvers, to learn rules
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and machine learning. We focus on inductive logic programming (ILP), a form of inductive program synthesis which learns logical rules from data. The focus of this position is to develop ILP/program
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Finnish Sign Language. The responsibilities of the appointee include developing approaches that use automatic speech recognition, computer vision models and other computational methods to annotate the data
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vision models and other computational methods to annotate the data, and verifying the quality of these annotations using human-in-the-loop approaches. In addition to conducting research, the post-doctoral
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(linking phenotypes, imaging, cytometry, or other readouts to transcriptomics) Statistics / machine learning for biological inference (model validation, differential state testing, embeddings/classifiers
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-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning
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spoken and written English; Research interests close to the project scope and prior experience of computational physics and/or chemistry as well as machine learning/artificial intelligence are a merit. The
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. The candidate should hold PhD degree in microbiology, bioinformatics or related field, and good written and verbal communication skills in English are necessary. The postdoctoral position is for 4 years starting
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) Application Deadline 28 Feb 2026 - 00:00 (UTC) Country Finland Type of Contract Other Job Status Full-time Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job
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the candidate's background and interests, ensuring a collaborative and engaging research experience. We seek candidates who have completed a PhD in ecological statistics or environmental economics or a related