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data combined with focussed innovation in statistical and computational methods including machine learning to advance our understanding, treatment, and prevention of human disease. Information
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Do you enjoy finding solutions to integrate and analyse large data sets of biodiversity dynamics and their drivers? Are you creative and able to couple various data flows and integrated modelling
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in AI, bioinformatics, statistics, computer science, data science, machine learning, epidemiology, or related fields with a relevant PhD degree (required). Experience with analyzing large genetic data
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Design invites applications for a PhD stipend in the field of secure machine learning within the general study programme Electronic and Electrical Engineering; as per November 1, 2026, or as soon as
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-preserving machine learning, distributed training, large language models, agentic AI, or cryptographic protocols is advantageous but not a requirement Qualification requirements PhD stipends are allocated
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-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
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students to analysing multimodal learning data (e.g., individual as well as collaborative verbal interactions, student gestures, task and activity sequences) and evaluating long-term learning outcomes. As
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science, medicine, or a related field. Excellent programming skills in Python and/or R. Experience with data curation, large-scale datasets, and genetic and machine learning methods. Interpersonal skills and
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: Conceptualisation and synthesis of data integration and visualisation workflows. Data management and the development of knowledge graphs Development and application of AI and machine learning methods and pipelines
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projects involve large-scale population cohorts, single-cell genomics, statistical genetics, functional genomics, machine learning, and clinical translation. We are a diverse and international team