Sort by
Refine Your Search
-
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
-
and machine learning. We focus on inductive logic programming (ILP), a form of inductive program synthesis which learns logical rules from data. The focus of this position is to develop ILP/program
-
-effectively predicting the rate of massively multicomponent organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning
-
organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning and artificial intelligence methods, targeted validation
-
applied industrial economics with ample opportunities for individual and collaborative research, acquisition of external research funds, and supervising MSc and PhD students. We seek candidates who have
-
funds, and supervising MSc and PhD students. We seek candidates who have completed a PhD in economics or agricultural economics, who have solid training in econometrics and applied microeconomics and
-
have solid skills in programming and working with libraries for training and using machine learning models. Previous experience in managing large volumes of data and high-performance computing is
-
The position requires: A doctoral degree relevant to the project (e.g., biology, genomics, evolutionary biology) Less than seven years since obtaining the PhD Ability to conduct independent scholarly work Strong
-
(linking phenotypes, imaging, cytometry, or other readouts to transcriptomics) Statistics / machine learning for biological inference (model validation, differential state testing, embeddings/classifiers
-
volumes of audiovisual data is essential. The appointee must have solid skills in programming and working with libraries for training and using machine learning models. Previous experience in managing large