Sort by
Refine Your Search
-
Listed
-
Employer
-
Field
-
, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
-
), and physiological parameters in the study of animal behaviour; a strong background in data analysis using R, preferably experience with Bayesian statistics and social network analysis; lab experience
-
, methodologies, and information derived from Bayesian modeling, data science, cognitive science, and risk analysis. Its primary objective is to create advanced forecasting models, generate meaningful indicators
-
of metabolic and cellular properties Phylogenomic analyses of obtained MAGs, including extraction and evaluation of marker genes, performing ML and Bayesian analyses of (concatenated) marker gene sets using
-
to demonstrate at conceptual level some of the features proposed where needed; provide a cost estimate of the various options meeting each requirement. You will also have the opportunity to study the GS&O
-
or computer vision); A strong scientific track record, documented by publications at first-tier conferences and journals (e.g., NeurIPS, ACL, ICLR, EMNLP, NAACL or COLM); Have excellent programming skills; Have
-
real-time, predictive control frameworks for ankle exoskeletons that regulate calf muscle-tendon forces during human locomotion. A central goal is the short-term (millisecond-scale) estimation and
-
, and how can uncertainty estimation, constraints and traceability be embedded? How will your proposed research contribute to ESA and, more generally, Europe’s long-term security resilience and strategic
-
at interdisciplinary conferences alongside a team of psychologists, historians, and computer scientists. What do you have to offer Must-have: You have completed a PhD in psychology, computational social science
-
). SLT and the estimation of such quantities has recently led to many applications, ranging from model selection and uncertainty quantification, over detecting phase transitions in machine learning models