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Instituto de Investigação e Inovação em Saúde da Universidade do Porto (i3S) | Portugal | about 1 month ago
autonomy, in microscopy techniques such as live-cell imaging; Good analytical skills, critical thinking, and problem-solver mindset; Knowledge of Python programming language; Excellent oral and written
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, extinctions, and environmental change; ● Running simulations and scenario analyses to explore how different discounting rules or time preferences shift optimal conservation choices; ● Fitting models
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relevant programming languages, particularly python Knowledge of, and experience from, the maritime industry Knowledge of generative models, reinforcement learning, or geometric deep learning. Experience
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skills in relevant programming languages, particularly python Knowledge of, and experience from, the maritime industry Knowledge of generative models, reinforcement learning, or geometric deep learning
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application! Your work assignments You will work on X-inactivation and allele-specific DNA methylation in different contexts, including X-linked diseases, autoimmune disorders, and hereditary cancer as part of
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EU MSCA doctoral (PhD) position in Materials Engineering with focus on computational optimization of
quality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e. g. random forest (RF), artificial neural network (ANN)) will be applied using the parameters
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. Molecular analysis of different cohorts of lung cancer patients. Animal experimentation (generation of PDX, humanized PDX, xenografts and genetically modified models), use of these models as a preclinical
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optimization to find the optimal set of parameters that improve process performance and material quality. Secondly, different machine learning strategies based on traditional supervised learning techniques (e.g
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learning, statistical estimation methods, software tools, and big-data frameworks. Programming languages such as e.g. Python, C++, and LABVIEW. Emission control rules and regulations in the shipping industry
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confirmed that different combinations of depth, rugosity, sea surface and bottom temperature, bottom flow speed and turbidity explain the different gorgonian's distribution. The objective of the present