Sort by
Refine Your Search
-
Listed
-
Country
-
Field
-
deep learning. You will support the development of an improved forest RTM that can exploit LiDAR full-waveform data along with hyperspectral signatures. You will plan and carry out field campaigns in
-
candidate will have recently completed (or be close to completing) a PhD in Computer Science, Machine Learning, Natural Language Processing (NLP), or a related field, with a thesis focused on AI, specifically
-
the long term. Is Your profile described below? Are you our future colleague? Apply now! Education PhD degree in remote sensing, preferably with a doctoral thesis on RTM inversion or deep learning in remote
-
neuroscience, and brain-computer interfaces, machine learning and deep learning, statistical modelling, regression methods, and uncertainty quantification, calibration, interlaboratory comparisons, and
-
future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
-
, training deep learning models on large satellite datasets, and contributing to the integration of forecasting workflows into the IceBox system. The candidate will work closely with an interdisciplinary team
-
on machine and deep learning methods for analyzing the heterogeneity of microbiota and inferring activities of biological pathways. The Institute provides an international and interdisciplinary research
-
, and train deep learning models on the resulting data to design new antibiotic compounds that evade both current and likely future resistance mechanisms. Your computational work will directly steer
-
future. Fueled by curiosity and a deep sense of duty, they contribute invaluable insights to research and teaching, enriching our society. Are you inspired and driven by the desire to make a meaningful
-
projects within the CUS related to urban sustainability, environmental monitoring, and urban resilience. Key Duties • Design and implement machine learning and deep learning models for hydrological