12 learning "https:" "https:" "https:" "https:" "https:" "https:" "Computer Vision Center" PhD positions at Utrecht University
Sort by
Refine Your Search
-
PhD position: Develop Hybrid Machine Learning for Global Soil Mapping Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline: 6
-
connections between individuals, groups, and institutions contribute to new pathways to and forms of social cohesion. See https://socion-program.org/research/ For more information about this position
-
mentality. We are looking for you, if you have: a (research) master’s degree in Artificial Intelligence, Computational Linguistics, NLP, Computer Science, Data Science, or a related field, with a strong
-
burial of microplastics and POC in currents down submarine canyons; incorporate these mechanisms into an existing computer model for sediment flux through submarine canyons; and validate the model on
-
, you will evaluate the social feasibility of implementing solutions in real-world settings, working closely with partners like the Delta Climate Center and local stakeholders. Methods will include
-
inclusion succeeds, where disparities persist and how universities can create more equitable learning environments. Your job IThe Department of Developmental Psychology has a job opening for a PhD
-
engagement and participatory research with quantitative system modelling of our research group; experience with systems modeling and computer programming (e.g. Python); strong communication and scientific
-
candidates with: An MSc degree in (or nearing completion of) an MSc program in Geoinformatics, Computer Science, (Quantitative) Geography, (Spatial / Geographic) Data Science, Environmental Science, or a
-
collaborative environment in which you have the freedom to work independently, while also contributing to a shared goal. You take a mastery-oriented approach to your own learning and development and are eager
-
plant growth, physiology and disease development; profile root and leaf microbiomes using amplicon sequencing; analyse integrated microbiome and phenotyping datasets; contribute to machine-learning