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Driven Discovery. Job Responsibilities: Analyze biomedical data with minimal supervision by performing advanced analysis, algorithm implementation, programming, and quality check. Assist senior analysts
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Learning with Graphs led by Prof. Nils M. Kriege. Our research focuses on the development of new methods and learning algorithms for structured data. Graphs and networks are ubiquitous in various domains
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analysis tools Investigating channel impairments, propagation effects, and mitigation techniques Designing and evaluating transmission schemes, signal processing algorithms, and communication architectures
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-assisted tools leveraging large language models (LLMs) to support community-based fact-checking Designi and evaluate methods to improve the robustness of algorithms used in community-driven fact-checking
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medicine. The lab invented the RNA origami method [1] and have developed basic algorithms and software for RNA design. However, there is a great need to develop new software for the design of advanced RNA
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maintenance, process modelling and optimisation algorithms, as a strategic enabler across multiple industrial sectors (manufacturing, energy-intensive industries, built environment, logistics, etc.). Your key
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(CRISPRi/a, Perturb seq, combinatorial screens), single cell and spatial omics, metabolomics, and immunopeptidomics. The successful candidate will pioneer assay-algorithm co design, demonstrate cross
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hospitals. PRIMARY DUTIES AND RESPONSIBILITIES: The qualified candidate will focus on developing new algorithms, including agentic artificial intelligence approaches, for the clinical integration
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, and protein structure prediction by artificial intelligence algorithms. The goal is to generate functional models of multimeric protein complexes and how they assemble as a guide to understand disease
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for a candidate capable of developing ML models and optimization algorithms specifically designed for highly dynamic satellite communication (SatCom) systems that can handle networks of varying sizes and