Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
information about the role, please contact Prof. Radu State Your profile Strong background in AI, machine learning, or multi-agent systems, ideally with interest in financial systems, decentralized ledgers
-
hydrodynamics for novel marine vehicles, including large ships and small AUVs and offshore renewable energy systems including offshore wind. You are expected to perform advanced computational fluid dynamics
-
of the following aspects of quantitative data analysis: experimental research designs, survey design, large/longitudinal datasets - and strong motivation and curiosity to enlarge these skills further • Very good
-
Leibniz-Institute for Plant Genetics and Crop Plant Research | Neu Seeland, Brandenburg | Germany | 23 days ago
architecture of important crop traits like grain yield heterosis. In the era of large population size and dense genomic data such as whole-genome sequencing, new algorithms are needed to remove the bottleneck
-
proteins in the mixture together define the key properties of these systems. Predicting these properties by only studying their components might seem impossible... but that is what we aim to do in the Big
-
Intelligence) is being expanded into a leading German AI competence center for Big Data and Artificial Intelligence (AI). TUD Dresden University of Technology embodies a university culture that is characterized
-
afraid of combining neurobiology and chemistry. You have good statistical skills and experience with analyzing big data (e.g. RNA-seq, spatial transcriptomics). You like to work in a diverse setting and
-
You will join the EPSRC-funded project “Behavioural Data-Driven Coalitional Control for Buildings”, pioneering distributed, data-driven control methods enabling groups of buildings to form
-
-WISE NEST project. This position will be with the Division of Artificial Intelligence and Integrated Computer Systems (co-PI: Prof Fredrik Heintz). We will strive for a tight collaboration between the
-
of innovative computational methods using Big Data, Behavioural Science and Machine Learning to understand behaviour through the lens of digital footprint/“smart data” datasets, cutting across sectors ranging