Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
Analysis Plans. •Experience with missing data methods and causal inference. •Experience with Bayesian methods and machine learning approaches. •Experience with REDCap and clinical research databases
-
Brandenburgische Technische Universität Cottbus | Cottbus, Brandenburg | Germany | about 2 months ago
candidate is expected to incorporate innovative approaches into their research that connect classical probabilistic models with modern deep learning architectures. Examples include Bayesian deep learning
-
Implementing Bayesian networks and uncertainty quantification techniques to account for sensor noise and model confidence limits Designing, training, and fine-tuning computer vision models to extract clinically
-
., R, Python, Julia), preferably including scientific software development. Knowledge of ecological statistics (frequentist and/or Bayesian), particularly spatial statistics and nonlinear models
-
, including active learning or Bayesian optimization. Experience with imaging, time-series or high-dimensional data. Exposure to crystallography or structural biology. Experience with multimodal datasets and
-
Bayesian methods, are encouraged to apply. Minimum Qualifications PhD in Statistics or closely related fields with documented research in statistics For the Assistant Professor position, candidates must
-
to have experience in several of the following areas: data processing, statistical analyses, R software, regression models, process-based models such as DSSAT or APSIM, Bayesian statistical analysis
-
editorial oversight. Familiarity with the concepts of Bayesian benchmark dose (BMD) modeling Special Instructions to Applicants: For full consideration, applications for job number 540019 should be both
-
, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible
-
modelling, ecological forecasting, time-series analysis, Bayesian statistics, and statistical programming (primarily in R and Stan). You will gain experience working with large, long-term, multidimensional