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field. Strong foundations in machine learning and familiarity with current AI tools and practices. A solid understanding of large language models, in particular their reliability, security, and
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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Health Data Sciences and Informatics (OHDSI, https://ohdsi-europe.org ) initiative, dedicated to bring out the full value of observational health data through the OMOP Common Data Model and large-scale
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. The research will involve training machine-learning models on large structure and sequence datasets and integrating membrane-specific biophysical constraints to enable the design of membrane proteins and
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about the PhD fellowship, please contact the principal supervisor. General information about PhD studies at the Faculty of Health and Medical Sciences is available at the Graduate School’s website: https
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infrastructures. • Experience in advanced statistical analysis and the processing of large volumes of data. • Participation in international scientific collaborations. Complementary Training • Knowledge of machine
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of flexible energy resources (e.g., heat pumps); knowledge of mathematical modelling, optimization, machine learning, or data analytics (experience in one or more is desirable); strong scientific programming
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, resulting in inconsistencies across soil properties and underperformance in data-scarce regions. This PhD project will develop next-generation machine learning methods for geospatial prediction by integrating
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modelling, machine learning, or microfluidics. They will also have excellent communication, organisational and problem-solving skills, and a strong interest in interdisciplinary quantitative biology
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of machine learning and clinical oncology, with access to a large multimodal research dataset, substantial GPU resources, and a collaborative scientific environment. Your tasks Design and implement LLM-based