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Chemistry, Polymer physics or a closely related discipline are encouraged to apply. Preferably, the candidate has been studying any of these topics: statistical physics, computer simulation methods, and
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the following PhD project within its MKT research programme at ERIM: · PhD in Consumer Behavior · PhD in Quantitative Marketing · PhD in Fair Machine Learning in Marketing Other fully
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unique opportunity to contribute to the technological foundations for tomorrow’s machine learning. Your job In the ERC project FoRECAST, we aim to develop theory (e.g., new probabilistic and differential
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eager to apply and valorize scientific results in this field in high-tech domains such as semiconductor machines and robots, together with highly innovative companies? Would you like to work in a team of
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University Medical Center Utrecht (UMC Utrecht); yesterday published | Netherlands | about 2 months ago
, epidemiologists, clinicians and lab researchers, with expertise in the field of prediction modeling, longitudinal data analysis, statistics, data science, machine learning, AI, organoid models and cystic fibrosis
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explore how artificial intelligence can be used to predict the response to therapy in cases of acute rejection of the kidney graft. Using machine-learning techniques, you will assess kidney transplant
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first line treatments for OSA, and works by delivering pressurized air to keep the airway open. Traditionally, titration of optimal PAP parameters was done in sleep labs, but home titration using auto
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recent advances in machine learning and browser automation, the project aims to provide tools, techniques and datasets to effectively address these threats. As a PhD candidate, you will play a key role in
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at TU Delft. In this project we also work together with experimental groups at TU Delft and beyond. The Delft Bioinformatics Lab has strong algorithmic and machine learning expertise, with a profound
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theory, discrete optimization and machine learning. In this PhD position you will focus on strain-aware genome assembly, variant calling and strain abundance quantification for viruses, bacteria and yeasts