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Field
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modelling, advanced AI algorithms, and decision-support tool development for various hydrogen technologies-based energy systems. Responsibilities will include programming, analysing and interpreting data, and
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to apply social science research methods to area studies, moving beyond traditional linguistic-cultural approaches while avoiding purely universalist, theory-driven models of explanation, A completed Ph.D
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biopsies and in a newly developed swine ALS model. The postholder will lead wet-lab experimental work, across biophysical (MANT-ATP) and functional genomic assays (methylome, transcriptome and proteome
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individual muscle fibre, this project will determine the subtype-specific dysregulation of SkM in ALS, both in human patient biopsies and in a newly developed swine ALS model. The postholder will lead wet-lab
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systems modelling including technical knowledge (e.g., in data science, input-output modelling, applied economic modelling, environmental and ecological assessments, GIS, comparative risk assessments), as
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systems modelling including technical knowledge (e.g., in data science, input-output modelling, applied economic modelling, environmental and ecological assessments, GIS, comparative risk assessments), as
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subtype-specific dysregulation of SkM in ALS, both in human patient biopsies and in a newly developed swine ALS model. The postholder will lead wet-lab experimental work, across biophysical (MANT-ATP) and
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(e.g., Reinforcement Learning, Agent Based Modelling) to join our team full-time as part of a large international collaboration of European researchers (incl. Tobias Dienlin, Veronica Kalmus, Adrian
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, BRCA2, and PALB2. Through advanced single cell genomics, in vivo modelling, and immune profiling, the team will study early molecular and cellular changes that occur in high-risk breast tissue. The team
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, and enabling data-driven improvements in patient care. You will have opportunities to apply foundation models—including large language models (LLMs) to real-world clinical data. You will work with well