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, providing direct feedback on theory analysis and further predictions. The goal is to develop a theoretical and computational approach that has strong predictive power for finding completely new types
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theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive dynamics, and population genetics. This position is part of the interdisciplinary consortium
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Postdoctoral Researcher in ML for Dynamical Systems Representation, Prediction, and State-estimation
Engineering at Aalto University explores synergies between nonlinear control theory and machine learning to provide formal guarantees on performance, safety, and robustness of complex systems. We are a part of
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quantitative predictions testable against empirical data from diverse ecological contexts. We use methods from theoretical evolutionary biology, including optimal control theory, life history modelling, adaptive
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., & Pihlainen, S. (2025). Controlling land-use change with a nature loss fee. Available at SSRN 5154007. Rodrigues AV, Rissanen T, Jones MM, Huikkonen I-M, Huitu O, Erkki Korpimäki E, Kuussaari M, Lehikoinen A
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resources are used to develop empirically founded theories of multimodal communication in the domain of everyday cultural artefacts. The data studied in the project includes, for example, school textbooks
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combinations of multiple ‘modes’ of expression. These methods and resources are used to develop empirically founded theories of multimodal communication in the domain of everyday cultural artefacts. The data