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); Agentic AI: Exploring multi-agent systems and their dynamics; Explainable AI: With a particular emphasis on mechanistic interpretability. Invent, evaluate, and publish novel algorithms, aiming
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: developing systems where algorithmic decisions can be traced and audited; ensuring that outcomes are verifiable against standards and rules; (ii) transparency and explainability: creating interpretable models
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network data science algorithms for mining molecular multi-omics and medical data to improve multiple tasks of precision medicine and discover new precision therapeutics. The successful candidates will work
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computational algorithm(s), perform data analysis, collaborate with biologists and physicians. He or she will lead multiple projects, work in a multidisciplinary environment and present/publish the results in
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us. II. Positions and Requirements Position 1: Scientist in Intelligent Biobreeding Algorithm Responsibilities: Lead the formation and direction of an interdisciplinary team to integrate AI
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DC-26094– POSTDOC/DATA SCIENTIST – AI-DRIVEN CLIMATE RISK MODELLING AND EARLY WARNING SYSTEMS FOR...
applicant will contribute to the AIGLE project by: · Developing innovative scientific Deep Learning/Machine Learning algorithms for flash flood forecasting. · Contributing to the collection
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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