Sort by
Refine Your Search
-
initio reference databases for training of machine-learned models. Documented experience in developing, training, evaluating and validating machine-learned interatomic force fields for atomistic
-
-Chem • You will be contributing to the development of machine learning models used on data from Poleno Jupiters, applying Python and machine learning. • The position will focus on implementing
-
. The work combines physics-based thermal design and process-level system simulation with high-fidelity computational fluid dynamics and fast reduced-order and machine-learning models, so that the final design
-
Background in machine learning or deep learning methods, including Graph Neural Network (GNN) Experience with Large Language Models (LLMs) applied to biological data collection, extraction, and standardization
-
science, energy engineering or a closely related field. Solid background in thermodynamics, transport phenomena, and process modelling and computer programming with documented experience in developing and applying
-
statistics, AI and machine learning methods, including demonstrated experience in analysing multiple global change drivers, e.g. land use intensity, climate change, nitrogen deposition. Proven capability
-
in devising and analyzing AI models, preferably including XAI methods, with a track record including publications in renowned venues of the Machine Learning and Data Mining field. Your application
-
postdoctoral researcher to join an interdisciplinary team developing deep learning models for antimicrobial resistance (AMR) detection directly from MALDI-TOF mass spectrometry data. The project is funded
-
populations. Apply Artificial Intelligence (AI) methods including deep learning (DL) models and supervised and unsupervised machine learning (ML) methods for integration and for Genome-2-Phenome (G2P) and risk