Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
-computer interaction, social networks, fairness, and data ethics. Our research is rooted in basic research and centres on mathematical models of the physical and virtual world, as a basis for the analysis
-
; representation from three continents). Our lab’s strength lies in integrating multiple disciplines: Single-cell microfluidics Quantitative (image) analysis Mathematical modelling Microbiology Molecular biology We
-
, Mathematics, Physics, or a closely related field. Proficiency in machine learning libraries (e.g, scikit-learn, PyTorch, and transformers) and data analysis tools (e.g., pandas, NumPy, and CuPy). Hands
-
Okinawa Institute of Science and Technology, Pure Mathematics Position ID: OIST -PDLS2026 [#26851] Position Title: Position Type: Postdoctoral Position Location: Okinawa, Japan [map ] Subject
-
learning or applied mathematics. Required skills and qualities: - Fluency with Python programming for data analysis or machine learning, - Knowledge of statistical or probabilistic modelling techniques
-
assigned to the research project "Randomization of Surrogates for the Quantification of Domain Uncertainty Propagation in Cardiovascular Models" as part of the Berlin Mathematics Research Center MATH+. The
-
Ph.D. or equivalent degree in mathematics, physics, computer science, bioinformatics, or a related field Experience in developing deep learning models Ideally, prior experience in analyzing biological
-
spectrometry-based metabolomics data, in part based on generative AI models of chemical structures. The position is available starting July 2025, and will remain open until excellent fits are found.The
-
character. The Faculty of Science, Technology and Medicine (FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer
-
networks. The research will employ mathematical modelling and computer simulation to identify synaptic plasticity rules which enable effective learning in large and deep networks and is consistent with