-
PhD position: Global soil mapping with process-informed machine learning Faculty: Faculty of Geosciences Department: Department of Physical Geography Hours per week: 36 to 40 Application deadline
-
degree in AI, Computing Science, Mathematics, or Data Science. Strong coding, communication and organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch
-
organizational skills. Demonstrable experience with using machine learning packages (e.g., PyTorch). Completed academic courses in AI or machine learning. We consider it an advantage if you bring experience with
-
. You will combine technical work on machine learning with qualitative analysis of how AI systems are interpreted and used in organisational decision-making. Join the Human-Centred Computing group
-
the course of the ongoing AI revolution. Your job Hybrid Intelligence (HI) is the combination of human and machine intelligence, expanding human intellect instead of replacing it. HI takes human
-
causal inference, machine learning, text analysis, or large-scale data integration. You support ODISSEI users via consultations and collaborative research, train researchers through workshops, and mentor
-
full projects, followed by the analysis, interpretation and reporting of data and results. You will pro-actively promote proteomics services and acquire new collaborative projects. The projects will vary
-
researchers to design and develop a wide range of innovative projects, for example involving causal inference, machine learning, text analysis, or large-scale data integration. You support ODISSEI users via
-
the ESCALATION study external link on early-onset breast cancer; apply advanced statistical, and machine-learning methods to identify and validate environmental determinants of breast cancer risk; integrate
-
, Introduction to Python, making figures using GGplot2 and basic machine learning. These courses are offered to PhD candidates through the PhD Course Centre of the Graduate School of Life Sciences . In