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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).

Number of UnicornsDESI - Compare two indicatorshttps://digital-decade-desi.digital-strategy.ec.europa.eu/datasets/desi/charts/compare-two-indicators?indicator=desi_dsk_ab&breakdown=ind_total&unit=pc_ind&country=NL,LU,PL,PT,SK,ES,SE&periodY=desi_2024&periodX=desi_2024&indicatorX=desi_unicorns&indicatorY=desi_da&breakdownY=ent_all_xfin&breakdownX=total&unitY=pc_ent&unitX=nb_unicornsDefinition and scopes:Compare two indicators using a scatter-plot chart. Each country appears as a point whose coordinates are its values on the two selected indicators. It allows clustering countries according to their similarities along the two selected indicators and illustrates how much they are correlated. You can also zoom inside the chart.Key insights from the graph:Sweden (SE): Exhibits a high number of unicorns and a significant percentage of enterprises utilizing data analytics.Netherlands (NL): Shows a moderate number of unicorns with a relatively high adoption of data analytics among enterprises.Spain (ES): Has a lower number of unicorns but demonstrates a considerable percentage of enterprises engaged in data analytics.Poland (PL), Portugal (PT), Slovakia (SK), and Luxembourg (LU): These countries display varying levels of data analytics adoption among enterprises, with a relatively lower presence of unicorns compared to Sweden and the Netherlands.This comparison highlights the relationship between the emergence of unicorns and the adoption of data analytics across different European nations.