Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
Computer Science and Computer Engineering with specialisation in Information Systems. In the context of Prof. Fridgen's PayPal-FNR PEARL Research Grant and the FutureFinTech National Centre of Excellence in Research
-
language (e.g., Python, R, Rust, JavaScript) Experience with data analysis, statistical modeling, or machine learning techniques Familiarity with handling large datasets (e.g., using SQL) and data pipelines
-
implications of AI-enabled conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in
-
conversational guides for enhancing visitors’ learning and experiences in public educational environments. The PhD student will focus on addressing the challenge of visual blindness in large language models (LLMs
-
such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary geospatial or remotely sensed data. Quantifying uncertainty and correcting
-
evaluate them alongside newly developed approaches. Integrating methods such as case weighting, anomaly detection, and model-based prediction (e.g., geostatistics and machine learning), using auxiliary
-
and small, contribute to a better world. We look forward to receiving your application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with
-
center ScaDS.AI Dresden/Leipzig (Center for Scalable Data Analytics and Artificial Intelligence) is being expanded into a leading German AI competence center for Big Data and Artificial Intelligence (AI
-
application! Your work assignments We are looking for one PhD student working on generative AI/machine learning, with applications towards materials science. Generative machine learning models have emerged as a
-
questions. Given the uncertainties involved in food supply chains, we prefer candidates who have a background in (stochastic) optimization methods (e.g., machine learning, stochastic dynamic programming