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Field
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machine learning and statistics; experience with Gaussian process regression and/or probabilistic regression. Experience with normative modelling is an advantage. Proficiency in Python (and ideally C/C
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learning, computational mathematics, and probabilistic modeling, with direct applications in medical imaging and beyond. Details can be found in the link below. The position is suitable for disabled persons
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determination Experience in machine learning or artificial intelligence, for example, pattern recognition or neural networks Experience in analyzing large data sets Previous knowledge of climate or ocean modeling
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project activities and writing reports Your Profile MSc or PhD in a relevant field Experience with Machine Learning modeling Coding experience, preferably Python Basic knowledge of databases tools and
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software solutions based on knowledge graphs and ontologies Use of machine learning and LLM technologies to solve technical and engineering problems Planning, design, and implementation of project and
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Hybrid Crop Modelling Framework, integrating Process-Based Models (PBMs) with Machine Learning (ML) to enhance the accuracy and interpretability of crop yield forecasts, while evaluating key ecosystem
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engage with it in the future. Qualification Required Master’s degree (or equivalent) in Computer Sciences or a related field by the beginning of the project Experience in machine learning Ability
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-based AI model evaluation XAI in Physics-Informed Neural Networks (PINNs) Applications in a wide range of machine learning models, architectures, inference targets and data modalities Intersection
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fine-tune machine learning and deep learning models to extract meaningful patterns and predict metastatic behavior Collaborate closely with experimentalists for mechanistic dissection of multimodal
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or similar object-oriented programming languages. Experience with machine learning/statistical methods applied to time series data is advantageous. Familiarity with databases, containerization, and knowledge