Have you considered applying for any of the postdoctoral projects listed under?
Join Integreat leadership, mentors and administration at the information meeting to clarify doubts, ask relevant questions, or just to learn more about Integreat and living in Norway.
Integreat aims to attract applicants to the following projects in Mathematics and Informatics respectively.
Postdoctoral programme DSTrain at dScience – Centre for Computational and Data Science at University of Oslo has announced its second call for postdoctoral fellowship positions of 36 months each under the EU's Marie Sklodowska-Curie COFUND action.
Zoom link (Meeting: 63286634096)
Available postdoctoral projects under DSTrain 2025
Full list of available projects, detailed information, requirements, and application
DSTrain call 2025
Double descent and sufficiency
Double descent and sufficiency
Mathematics
Point of contact: Emil Aas Stoltenberg
Keywords: Bias variance trade-off, approximate sufficiency, overparametrisation
Bayesian machine learning
Mathematics
Point of contact: Geir Storvik
Keywords: Bayesian methodology, Machine learning, neural nets
Causality-Inspired Machine Learning
Causality-Inspired Machine Learning
Mathematics
Point of contact: Johan Pensar
Keywords: Causality, Machine Learning, Domain Generalization
Explainable and scalable statistical models for complex data
Explainable and scalable statistical models for complex data
Mathematics
Point of contact: Ingrid Hobæk Haff
Keywords: Parametric models, explainable models, algorithms, conflict, sensitivity, hierarchical models
Gradient boosting for time-to-event analysis
Gradient boosting for time-to-event analysis
Mathematics
Point of contact: Riccardo De Bin
Keywords: Gradient boosting, first-hitting-time models, informative censoring, structural missingness, survival analysis
Stochastic modelling of natural processes
Stochastic modelling of natural processes
Mathematics
Point of contact: Thordis Thorarinsdottir
Keywords: Machine learning, statistical modelling, extremes, climate risk
Separation of linguistic and factual knowledge in Large Language Models (LTG2)
Separation of linguistic and factual knowledge in Large Language Models (LTG2)
Informatics
Point of contact: Andrey Kutuzov
Keywords: Large Language Models, Machine learning, Natural Language Processing, Computational Linguistics
Actionable recourse and fairness over time (ASR2)
Actionable recourse and fairness over time (ASR2)
Informatics
Point of contact: Ingrid Chieh Yu
Keywords: Explainable AI, Transparency and Trust in AI, Circular Economy, Sustainable Product Lifecycle
Graph Neural Networks for Hierarchical Structured Learning (DSB5)
Graph Neural Networks for Hierarchical Structured Learning (DSB5)
Informatics
Point of contact: Adín Ramírez Rivera
Keywords: Graphs, Graph Neural Networks, Machine Learning, Hierarchical Learning, Hierarchy Discovery
Neural Networks' Learning via Sufficiency and Information Theory (DSB4-A)
Neural Networks' Learning via Sufficiency and Information Theory (DSB4-A)
Informatics
Point of contact: Ali Ramezani-Kebrya
Keywords: Machine Learning, Statistics, Information Theory, Digital Twins, Energy-efficient ML, Secure ML, Robust ML, Distributed Learning, Federated Learning, Systems, Neural Operator; Weather Prediction; Dynamical Systems; Partial Differential Equations
Sustainable, Efficient, and Secure Machine Learning (DSB4-B)
Sustainable, Efficient, and Secure Machine Learning (DSB4-B)
Informatics
Point of contact: Ali Ramezani-Kebrya
Keywords: Machine Learning, Statistics, Information Theory, Digital Twins, Energy-efficient ML, Secure ML, Robust ML, Distributed Learning, Federated Learning, Systems, Neural Operator; Weather Prediction; Dynamical Systems; Partial Differential Equations
Joint Physics-informed, Data-driven, and Digital twins for Complex Dynamical System Solvers (DSB4-C)
Joint Physics-informed, Data-driven, and Digital twins for Complex Dynamical System Solvers (DSB4-C)
Informatics
Point of contact: Ali Ramezani-Kebrya
Keywords: Machine Learning, Statistics, Information Theory, Digital Twins, Energy-efficient ML, Secure ML, Robust ML, Distributed Learning, Federated Learning, Systems, Neural Operator; Weather Prediction; Dynamical Systems; Partial Differential Equations
Understanding Emotion with Machine Learning (DSB3)
Understanding Emotion with Machine Learning (DSB3)
Informatics
Point of contact: Ali Ramezani-Kebrya and Adín Ramírez Rivera
Keywords: Machine Learning, Human Memory and Emotion, Time Series, Anomaly Detection, Complex Data
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