Sort by
Refine Your Search
-
Listed
-
Category
-
Country
-
Program
-
Field
-
experimental goals are translated into executable protocols, results are processed and stored with full traceability, and Bayesian optimization, active learning or related machine-learning methods select the
-
in vivo perturbation experiments to uncover how genes shape immune-cell states and predict how the immune system responds to interventions. This tight integration of advanced machine learning and
-
Max Planck Institute of Molecular Cell Biology and Genetics | Dresden, Sachsen | Germany | 25 minutes ago
Dynamics * Information Theory and Network Analysis * Mathematical Biology * Mathematical Machine Learning / Theory of Deep Learning * Mathematical and Theoretical Physics * Stochastic Topology Your
-
formulation, molecular-level characterization, and machine learning to develop predictive models linking chemical descriptors to adhesive properties. The work includes the development of a structured
-
big-data lake ecology research using techniques from statistics and computer science (e.g., statistical modeling, machine learning, data mining). Knowledge of open science tools (e.g., Github, Zenodo
-
Mathjobs.org | about 1 month ago
focuses primarily on geometric analysis, spectral geometry, and differential geometry on singular spaces, preferably with connections to Finsler geometry, discrete geometry, or machine learning. Your
-
focuses primarily on geometric analysis, spectral geometry, and differential geometry on singular spaces, preferably with connections to Finsler geometry, discrete geometry, or machine learning. Your
-
17 Aug 2026 Job Information Organisation/Company Sofia University "St. Kliment Ohridski" Research Field Computer science Researcher Profile Established Researcher (R3) Positions Postdoc Positions
-
/jobs/363159/postdoc-on-causal-machine-learn… Requirements Additional Information Website for additional job details https://www.academictransfer.com/363159/ Work Location(s) Number of offers available
-
opens the door to many new experiments. Through the ErUM-Data and LEAPS-funded proposals, we develop resource-efficient and reliable Al and machine learning methods to advance ML for photon science