Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
-
Field
-
to climate change and variability Hydrological processes in organosols and peat-affected soils Modeling Hydrological Extremes Using Machine Learning Spatial and time distribution of precipitation within
-
of dissertation topics: Developing Remote Sensing–Based Indicators of Landscape State and Change Using Data-Efficient Machine Learning Across Scales Profile of the graduate The graduates have deep theoretical
-
of machine learning technologies, including large language models within the Department of Food and BioResource Technology, with a special focus on technologies applicable in so-called developing countries. As
-
programs at the Faculty of Horticulture, aiming to acquire the theoretical and practical knowledge necessary for mastering professional studies. The teaching is primarily focused on issues related
-
educator in statistics and/or modern data analysis (including ML/DL). Research scope – expertise in any of the following areas • statistics, data analysis, and information theory, • machine learning, deep
-
will be integrated with statistical and machine-learning methods to classify polarity states and identify quantitative signatures predictive of metastatic behavior. The project will deliver transferable
-
description The project will use bioinformatic analysis together with comparative approaches to individual cells, and machine learning to investigate how the vertebrate head evolved and what mechanisms control
-
antibodies and anti-CD20 CAR T-cells form the backbone of treatment for mature B cell malignancies. The project focuses on elucidating changes in gene expression within the tumor microenvironment of chronic
-
animals and humans, contacts with the environment are not avoided and sometimes even actively sought. We will deploy this inspiration from biology to design truly robust machines with distributed control
-
, dimensionality reduction and/or machine learning methods (e.g., Lasso, ridge regression) is highly desirable. Familiarity with neurostimulation, Parkinson’s disease, or neuropsychological assessment tools is