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learning by using Bayesian learning principles. Among other things, Bayesian learning gives AI systems the ability to quantitatively express a degree of belief about a prediction or statement. By bridging
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Objects and Visual Studies” doctoral program of the BerGSAS, https://www.berliner-antike-kolleg.org/en/bergsas/index.html We welcome applications from highly qualified graduates from the fields
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and regulatory context. Objectives Develop an agent-based modelling and stakeholder analysis toolkit to capture the perspectives, needs, and regulatory constraints of main stakeholders. Design modular
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risk development. These projections will enable the identification of sustainable adaptation strategies and support policy development. Objectives Develop an Agent Based Model (ABM) that describes flood
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Computer vision (3D, detection, classification, segmentation, object tracking). Autonomy, creativity, and interest in real-world AI applications. Interest in agriculture or agri-food. What We Offer
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will be grounded in rigorous mathematics coupled with a sound understanding of the underlying earthworm ecology. Bayesian inference methodologies will be developed to estimate where and when behavioural
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research and soft robotics development. PhD project The PhD project will focus on the technical aspects of simulating the physics of the Drosophila larva body. The primary objectives include: Developing a
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”, led by Associate Professor Valeria Vitelli. Successful candidates will work on Bayesian models for unsupervised learning when multiple data sources are available, mostly tailored to the case
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creative – You bring substantial knowledge of statistical (e.g. Bayesian) methods, strong analytical skills, and creativity. Programming skills – You are proficient in Python and/or Matlab. Research
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system using deep learning (DL). The project’s objectives include generating training data from synthetic datasets and real-world images (cadaver and actual intraoperative THR images), developing a marker