Sort by
Refine Your Search
-
Listed
-
Employer
- Delft University of Technology (TU Delft)
- Eindhoven University of Technology (TU/e)
- European Space Agency
- Delft University of Technology (TU Delft); Delft
- Delft University of Technology (TU Delft); yesterday published
- Radboud University
- University of Amsterdam (UvA)
- University of Amsterdam (UvA); yesterday published
- Wageningen University & Research
- Amsterdam UMC
- Amsterdam UMC; Amsterdam
- Amsterdam UMC; 27 Sep ’25 published
- Delft University of Technology (TU Delft); 26 Sep ’25 published
- Eindhoven University of Technology (TU/e); Eindhoven
- Eindhoven University of Technology (TU/e); yesterday published
- Erasmus MC (University Medical Center Rotterdam)
- Erasmus MC (University Medical Center Rotterdam); today published
- Leiden University Medical Center (LUMC)
- Maastricht University (UM)
- Maastricht University (UM); yesterday published
- University of Amsterdam (UvA); 26 Sep ’25 published
- Wageningen University and Research Center
- 12 more »
- « less
-
Field
-
Bayes factor hypothesis tests in factorial designs. What are you going to do The envisioned projects will focus on the following activities related to Bayesian inference in factorial designs: Construction
-
on the following activities related to Bayesian inference in factorial designs: Construction and elicitation of informed prior distributions; Critical assessment of default prior distributions; Organizing a many
-
with a hybrid-electric propulsion system, targeted for entry into service by 2035. Given the rapid evolution of various battery technologies, it is currently uncertain which specific technology will be
-
Batteries, a project that aims to develop an airworthy battery system for an ultra-efficient regional aircraft with a hybrid-electric propulsion system, targeted for entry into service by 2035. Given
-
) multi-omics analysis for the identification of therapeutic targets (biomarkers or druggable targets). The candidate will also develop and implement AI-driven tools to predict disease prognosis and
-
-subject clinical and pathological information with morphological and molecular knowledge of induced pluripotent stem cells (iPSC) of PD(D)/DLB patients to identify molecular profiles and drug targets. We
-
mathematical background, including expertise in stochastic optimization (e.g. Markov decision theory and dynamic programming) and applied probability (Bayesian statistics). Excellent coding skills (e.g., in Java
-
) multi-omics analysis for the identification of therapeutic targets (biomarkers or druggable targets). The candidate will also develop and implement AI-driven tools to predict disease prognosis and
-
of induced pluripotent stem cells (iPSC) of PD(D)/DLB patients to identify molecular profiles and drug targets. We will collect skin biopsies from PD patients of whom brain pathological confirmation is
-
/hybrid implementations of AI algorithms, novel quantum computing algorithms, etc.); conduct theoretical studies to identify target EO use cases that could benefit from (hybrid) quantum computing solutions