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: Investigation of precipitation and crystallization processes in phosphorus recovery using experimental and numerical methods Supervisor: Prof. Sergiy Antonyuk C2: Experimental investigation and modeling
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Your Job: The PhD position is offered in the context of the HDS-LEE graduate school. We are looking for a highly motivated PhD candidate to join our world-leading research program in Earth System
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experience in machine learning and parallel computing Good organisational skills and ability to work both independently and collaboratively Experience with deep learning frameworks, such as Tensorflow
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method implemented into the EURAD-IM. In atmospheric chemistry modeling, the 4D-var method is a powerful tool to assess the state of the atmosphere and the corresponding emissions that are in compliance
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about state-of-the-art methods in machine learning, reinforcement learning and computer vision for the life sciences Your Profile: Excellent Master’s degree in engineering, computer
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Proficiency in at least one programming language, preferably Python; experience with scientific computing, numerical modeling, or machine-learning frameworks is an asset Strong analytical skills with a solid
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computer vision methods and their applications Your Profile: Excellent Master’s degree in engineering, computer science or mathematics (or a related field), with a focus on computer vision, image processing
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Your Job: Develop methods and workflows to construct robust co-regulation networks from large single-cell and spatial transcriptomics datasets Integrate ontologies and metadata (e.g., tissue, cell
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Bayesian computational statistics, differentiable programming, and high-performance computing, the project aims to deliver robust, interpretable, and scalable methods for metabolic flux analysis. You will
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, molecular simulations, and their application to bioprocesses and bioseparations Proficiency in at least one programming language, preferably Python; experience with scientific computing, numerical modeling