Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Field
-
. The most recent methodological research of the group includes algorithms for cell type deconvolution, high-resolution purification, and integration of single cell multi-omics data. This postdoctoral fellow
-
advanced AI algorithms to optimize and understand the optical properties of light-trapping surfaces. (more information can be found in the following News post ). You will work closely with colleagues both
-
data analysis experts. The main tasks include the analysis of complex biomedical data using modern AI methods, as well as the development of novel machine and deep learning algorithms to understand
-
programming of algorithms. The use of programming languages such as Python, R, SQL, and C++ will be a daily part of the project, and proficiency in these languages is required. However, additional datasets will
-
attacks Develop and implement ML algorithms to identify vulnerabilities and predict potential threats in supply chain systems Prepare project deliverables and disseminate results through high-quality
-
skills in data analysis, machine learning, as well as in mathematical and computational modelling? You will have the opportunity to investigate innovative solutions using machine learning algorithms and
-
disease into specific subclasses. You will develop AI algorithms to train models that predict if individuals (from which we create circuits) are prone to develop disease and to identify conditions that have
-
Restoration of Sensory and Motor Systems, https://sfb1690.uni-goettingen.de ) and the newly founded Else Kröner Fresenius Center for Optogenetic Therapies (EKFZ: https://ekfz.uni-goettingen.de ). Your tasks
-
, quoting reference number 487-25 to Prof. Dr. Heiko Wende, University Duisburg-Essen, Faculty of Physics, 47057 Duisburg, telephone +49 203 379-2838, e-mail: heiko.wende@uni-due.de . Information about the
-
sciences as well as a wide variety of amenities Contact for more information Martin Becker (https://bckrlab.org) +49 6421-28 25509 | martin.becker@uni-marburg.de We support women and strongly encourage them