Sort by
Refine Your Search
-
currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
-
2023 as the 10th VIB center, with the core mission to study fundamental problems in biology by combining machine learning with in-depth knowledge of biological processes. We aim to work towards
-
currently exploring a range of exciting topics at the intersection between computational neuroscience and probabilistic machine learning, in particular, to derive mechanistic insights from neural data. We
-
FWO-UGent funded bioinformatics postdocs: Unveiling the significance of gene loss in plant evolution
Integration of phenotypic data with omics analysis Explore machine learning and network analysis methods Profile Essential A PhD in Bioinformatics, Computational Biology, Evolutionary Biology
-
and building and maintaining machines and automation, preferentially with experience in plant biology. Job description Maintenance of automated phenotyping systems, containing conveyer belts or gripper
-
models combining machine learning, and physics-of-failure (PoF) approaches using in-situ data • You work on projects independently • You will present your work at international conferences and
-
Python or R A willingness to learn and apply machine learning approaches We offer A versatile and challenging job in a vibrant and world-class research environment operating at an international level
-
of predictive models for energy demand and production. These models will leverage techniques such as time series analysis and machine learning and will be integrated into a digital twin platform. The aim is to
-
working in a lab (placements during degree studies is sufficient) Basic computer skills (text processing, spreadsheet, presentations) Enthusiastic team player Basic understanding of immunology Desirable but
-
, criterion handling and machine learning. Topic The main research objective is to contribute to the development of responsible AI, with a strong focus on trust and confidence handling when dealing with data