Sort by
Refine Your Search
-
Listed
-
Category
-
Employer
-
Field
-
accumulate in groundwater and are difficult to clean up during drinking water production, posing significant concerns for society. Chemical identification in complex samples is time-consuming, necessitating
-
research focus will include some of the following topics: Advanced sensor fusion and multimodal AI models for robotic intercropping. Self-supervised learning will generate multimodal agricultural pre-trained
-
mathematical foundation of machine learning models. You will be responsible for developing scientific machine learning methodologies enabling new approaches for solving machine learning problems including
-
friendly catalysts and substrates. We use NMR spectroscopy for serendipitous discoveries and unbiased characterizations of molecules, their conversion, and their interactions in complex systems. Solvent
-
nanoparticles and reactions at the atomic-level by combining path-breaking advances in electron microscopy, microfabricated nanoreactors, nanoparticle synthesis and computational modelling. The radical new
-
to mechanical forces. We work with leading international groups on modeling and also conduct simulations at DTU. Our overarching goal is to understand and predict the mechanical behavior of metals during plastic
-
sustainable energy. Responsibilities and qualifications Biohybrid systems offer unique energy-efficient routes to harness solar energy for the fixation of CO2 and nitrogen into valuable, complex molecules
-
appointment from 1/9/2025 or as soon as possible thereafter. The position is for 3 years, and the workplace is in the Medical Biotechnology Section in Aalborg. Your work tasks You will be using the model
-
intelligence. This PhD project will leverage the power of field-programmable gate arrays (FPGA) to deploy machine learning models on the edge with low latency and high energy efficiency. This added intelligence