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and deploy advanced deep learning and foundation models for surgical scene understanding segmentation, tracking, and operator assistance. You will write, test, and optimise Python and C++ code for real
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sciences, AI, machine learning or related fields. Strong background and track record in the development of geospatial foundation models from multi-modal Earth Observations is essential as well as strong
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statistical modelling of high-dimensional data, e.g. penalised model selection and machine learning. Demonstrable understanding of RNAseq and gene expression analysis. Experience/skills handling and securely
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Functions Developing and implementing machine learning and deep learning models to analyze forestry, physiological, and ecological datasets Modeling plant growth, carbon allocation, stress response (e.g
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. Candidates with background knowledge and hands-on experience in mouse models, proteomics, 3Dorganoids,primary cells purification and culture skills are particularly welcome. Minimum Requirements: Ph.D
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contribution of the PhD will be the derivation of multilayered approaches for motion planning and control based on the XS-Graphs, where both model-based and learning-based solutions are foreseen. This includes
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), Computational Biology (stochastic and analytical models of gene expression), Signal Processing (machine learning, image and signal processing), Biophysics, Microbiology and Single-cell Biology (flow cytometry
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outcomes research and real-world data analytics, with a strong publication record. Proficiency in advanced data analytics, machine learning, and statistical modeling. Job Description: The Department
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perform prioritized Non-Targeted Assessment across diverse water matrices and case studies, while the AI4Science PhD will develop machine‑learning models that learn from and build upon these pNTA results
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, with a view to developing and carrying out the above-mentioned project and related scientific activities, with a particular focus on the development of analytical models (data science – machine learning