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Field
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). These collaborations enable practically relevant and breakthrough results. This team goal requires a quantitative model describing and predicting sperm motility under various conditions. You will develop the digital
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Machine Learning. Profile of the graduate The graduate displays deep theoretical knowledge in molecular and cell biology, genetics and virology, with focus on some specific branch of these scientific fields
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in C++ and/or Python is expected, and experience in model analysis and parameter optimisation is beneficial. Experience in machine learning and neural networks is desirable. The successful applicant
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the Arctic, experimental tests of climate driven changes in carbon export from land and turnover and release of greenhouse gases (CO2 and CH4 ) from headwaters, and use of machine learning and process-based
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descriptors to support machine learning model development to accelerate materials discovery: Perform high-throughput DFT and molecular dynamics simulations to investigate the thermodynamic, structural, and
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should hold a Master's degree in Computer Science, Artificial Intelligence, Computational Linguistics, Data Science, or a closely related field Solid background in machine learning and natural
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integrating modeling, machine learning (ML), and advanced control methodologies. The research will focus on designing AI-driven algorithms to assess battery health, predict degradation trends, and optimize
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. Previous experience with machine learning applications in molecular modelling, including experience with at least three of the following Python libraries: TensorFlow, PyTorch, JAX, RDKit. Previous
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willing to work in a collaborative environment. Preference will be given to those with (i) strong background in quantitative methods, geospatial methods, AI and machine learning; (ii) experience in high
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broad range of areas, including causal inference and time-to-event analysis, clinical trials, epidemiology, high dimensional statistics, infectious disease, machine learning and mathematical modelling