-
machine learning. We focus on inductive logic programming (ILP), which learns logical rules from data. We primarily use automated reasoning techniques, such as SAT/ASP/SMT/MaxSAT solvers, to learn rules
-
of Helsinki. The main research fields at the department are artificial intelligence, big data frameworks, bioinformatics, data analysis, data science, discrete and machine learning algorithms, distributed
-
the candidate's background and interests, ensuring a collaborative and engaging research experience. We seek candidates who have completed a PhD in ecological statistics or environmental economics or a related
-
across connected forest–lake ecosystems. By integrating multi-taxa field data, trait-based ecology, experiments, and advanced statistical analyses, TRACE aims to uncover how ecological processes propagate
-
in vivo genetic mouse models, advanced live and intravital imaging, engineered microchip models, primary cell co-culture systems and novel microscopy and analysis methods. The research will provide
-
cells, the main antigen presenting cells of the immune system. Our work is relevant for immune system related diseases such as immunodeficiency, inflammatory disorders and cancer. We use a wide range of
-
bacterial communities, we lack an understanding of how these changes are associated with plant health and growth. The main aim of this project is to experimentally study how phages can boost plant growth and
-
transition from rainfed to nature- and technology-based water management systems can be used to safeguard profitable, sustainable, resilient, and regenerative primary production in the pressure of global