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Call for Applicants in Deep Reinforcement Learning for Flow Control (CFD) – Linköping University, Sweden
Host
Mar 30, 2026 at 4:18 PM
MSCA-PF 2026: Call for Applicants in Deep Reinforcement Learning for Flow Control (CFD) – Linköping University, Swedenhttps://euraxess.ec.europa.eu/jobs/hosting/msca-pf-2026-call-applicants-deep-reinforcement-learning-flow-control-cfd-linkopinghttps://liu.se/en/employee/saesa86Hosting InformationOffer Deadline: Tue, 30 Jun 2026 - 23:59EU Research Framework Programme: Horizon Europe - MSCACountry: SwedenWe are looking for a highly motivated postdoctoral researcher to jointly apply for a Marie Skłodowska-Curie Postdoctoral Fellowship (MSCA-PF 2026) in the area of computational fluid dynamics (CFD) and machine learning.The project will focus on data-driven and reinforcement learning approaches for flow control, with particular emphasis on robustness and generalization across operating conditions. The research will be conducted using high-fidelity CFD tools (primarily OpenFOAM) combined with modern machine learning frameworks.The host environment is the Division of Applied Thermodynamics and Fluid Mechanics at Linköping University, Sweden. The group has strong expertise in CFD, turbulence, and data-driven modeling, with ongoing research on deep reinforcement learning for flow control and multifidelity methods.We are particularly interested in candidates with a strong background in one or more of the following areas:Fluid mechanics and turbulenceComputational fluid dynamics (CFD)Machine learning / deep reinforcement learningScientific computing and programming (Python, C++)The selected candidate will work closely with the host to develop a competitive MSCA proposal.Eligibility (MSCA rules apply):PhD degree at the time of the deadlineMaximum 8 years of research experience after PhDMust comply with the MSCA mobility ruleInterested candidates should send a CV and a short statement of research interests to the host (Saeed Salehi: saeed.salehi@liu.se).
Conference: The 10th International Conference on Deep Learning Technologies
General NEWS
Nov 29, 2025 at 11:22 PM
The 10th International Conference on Deep Learning TechnologiesJuly 17-19, 2026, Kunming, Chinahttps://www.icdlt.orgSubmission Deadline: March 1, 2026Notification of Acceptance: April 1, 2026Camera Ready Deadline: April 15, 2026Registration Deadline: April 15, 2026Conference Dates: July 17-19, 2026You are invited to attend 2026 10th International Conference on Deep Learning Technologies will be held during July 17-19, 2026 in Kunming, China. It's sponsored by Kunming University of Science and Technology, China, organized by the Faculty of Information Engineering and Automation of Kunming University of Science and Technology. It's to provide a valuable opportunity for researchers, scholars and scientists to exchange their ideas face to face in Deep Learning Technologies.We invite researchers from deep learning technologies to participate and submit their work to the program. Likewise, any work on deep learning that has a relation to any of these fields or potential for the usage in any of them is welcome. Please refer to the different submission categories under "Call for Papers" above for further details.Track 1: Deep Learning Model and AlgorithmRecurrent Neural Network (RNN) Sparse Coding Neuro-Fuzzy Algorithms Evolutionary Methods Convolutional Neural Networks (CNN) Deep Hierarchical Networks (DHN) Dimensionality Reduction Unsupervised Feature Learning Deep Boltzmann Machines Generative Adversarial Networks (GAN) Autoencoders Deep Belief Networks Meta-Learning and Deep Networks Deep Metric Learning Methods MAP Inference in Deep Networks Deep Reinforcement Learning Learning Deep Generative Models Deep Kernel Learning Graph Representation Learning Gaussian Processes for Machine Learning Clustering, Classification and Regression Classification ExplainabilityTrack 2: Machine Learning Theory and TechnologyNovel machine and deep learningActive learningIncremental learning and online learningAgent-based learningManifold learningMulti-task learningBayesian networks and applicationsCase-based reasoning methodsStatistical models and learningComputational learningEvolutionary algorithms and learningFuzzy logic-based learningGenetic optimizationClustering, classification and regressionNeural network models and learningParallel and distributed learningReinforcement learningSupervised, semi-supervised and unsupervised learningTensor Learning Deep and Machine Learning for Big Data Analytics:Deep/Machine learning based theoretical and computational modelsMachine learning (e.g., deep, reinforcement, statistical relational, transfer)Model-based reasoningTrack 3: Deep and Machine Learning ApplicationsDeep Learning for Computing and Network PlatformsRecommender systemsDeep Learning for Social media and networksDeep Learning in Computer VisionDeep learning in speech recognitionDeep Learning in Nature Language Processing,Deep Learning in Machine TranslationDeep learning in bioinformaticsDeep Learning in Medical Image AnalysisDeep Learning in Climate ScienceDeep Learning in Board Game ProgramsDeep and Machine Learning for Data Mining and KnowledgeTrack 4: Responsible AI, Security, and GovernanceAI safetyPrivacy preservationAlgorithmic FairnessFairness and ethicsClassification Explainability and Explainable AI (XAI)AI EthicsAI governanceGreen Deep LearningSynthetic Data GenerationSocial and Economic Impact of AISustainable AIAI Risk AssessmentThe accepted paper will be included into ICDLT 2026 Conference ProceedingsPUBLICATION HISTORYICDLT 2025 Proceedings - 979-8-4007-1852-6 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2024 Proceedings - 979-8-4007-1686-7 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2023 Proceedings - 979-8-4007-0752-0 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2022 Proceedings - 978-1-4503-9693-6 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2021 Proceedings - 978-1-4503-9016-3 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2020 Proceedings - 978-1-4503-7548-1 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2019 Proceedings - 978-1-4503-7160-5 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2018 Proceedings - 978-1-4503-6473-7 | ACM Digital Library | Ei & SCOPUS IndexICDLT 2017 Proceedings - 978-1-4503-5232-1 | ACM Digital Library | Ei & SCOPUS Index
2025 IEEE Deep Learning and Computer Vision
General NEWS
Jan 6, 2025 at 2:05 AM
Call for Papers2025 IEEE 2nd International Conference on Deep Learning and Computer VisionJinan|ChinaApr. 18-21, 2025Full paper submission Deadline: Feb. 10th, 2025Acceptance Notification: Mar. 26th, 2025Registration Deadline: Apr. 11th , 2025Conference Date: Apr. 18-21st, 2025Link: https://www.icdlcv.orgContact: icdlcv@126.comYou are kindly encouraged to contribute to and help shape the conference through submissions of your research abstracts and papers to DLCV 2025 conference. Topics of interest for submission include:Track 1: Deep LearningMachine LearningUnsupervised LearningDeep Reinforcement LearningConvolutional Neural Networks (CNN)Deep Hierarchical Networks (DHN)Recurrent Neural Network (RNN)Neuro-Fuzzy AlgorithmsAutoencodersGraph Representation LearningClustering, Classification and RegressionEvolutionary MethodsAutonomic ComputingUser ModelingInformation Retrieval and ReuseQuestion-AnsweringCognitive ArchitecturesDeep Learning Approach in Recommendation SystemsTrack 2: Computer VisionBig data and computer visionBiomedical image analysisImage and video codingImage and video retrievalRemote sensing imageComputational photographyOptimization and method of studySensor and displayData collection and performance analysisComputer vision of deep learningDocument image analysisCharacter identificationAnalysis, behavior identityVisual modelsVideo analysisMultimodal information processingVisual and languageMotion and tracking3D reconstructionHuman-computer interaction