Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
the development and implementation of machine learning (ML), computer vision (CV), large language models (LLMs), and vision-language models (VLM) to automate data extraction and interpretation for productivity
-
), ecological processes (primary productivity and decomposition rates), and greenhouse gases (3D-printed flux chambers), and investigating how we can use citizen science-driven data streams for model development
-
to modern genomic datasets that may involve hundreds of populations. He/she will also develop probabilistic GO models inspired from the Redundancy Analysis approach and extend it by introducing Neural
-
processes studied, specifically high moisture extrusion, drying, and ripening processes, useful to understand and control the mechanisms of texture development. - To study and model the relationship between
-
other empirical damage and vulnerability data. Couple the ABM with the Regional Flood Model (RFM) to describe temporal developments of flood risk considering adaptation decisions. Different adaptation
-
a powerful way for assessing forest stress and disturbances over large areas and to monitor forest vitality over time. This research uses remote sensing technologies together with physical models and
-
challenging properties of uncertainty, irregularity and mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and
-
to regenerative agriculture, including biological indicators that are often neglected in soil health monitoring programs. You will be part of a team of PhDs focusing on modeling soil processes at different scales
-
mathematical modelling? Then you could be the ideal PhD candidate for this position. Self-assembled structures of colloidal particles and/or polymers at a liquid-air interface can be spontaneously generated when
-
mixed-modality. It will examine a range of models and techniques that go beyond Markovian approaches, including state-space models, tensor networks, and machine learning frameworks such as recurrent