Sort by
Refine Your Search
-
are developed, modelled and controlled. You will create novel adaptative, physics-informed models that tightly integrate thermo-fluid dynamic laws, deep learning neural networks, and experimental data. A key
-
data analysis, programming, and biology. You will be part of a collaborative research team with deep experimental and analytical expertise, with access to advanced tumor models and state-of-the-art
-
multidisciplinary research in energy markets, optimization, game theory, and machine learning. Our team of 13 members (link ), from 10 different nationalities, values diversity and includes experts from a range of
-
fees and include a tax-free stipend (£19,237 pa. currently), for a period of 3.5 years. The successful candidate will be supervised by Prof. Kurt Debattista and Dr. Thomas Bashford-Rogers
-
spectrum, in topics in virology and immunology, and currently specializes in computational biology focusing on developing methods and applications of deep learning for protein sequence and structure, as
-
the accurate prediction of reaction enthalpies and activation free energies for all relevant intermediates. In this project, a deep learning and generative design toolchain will be developed resulting in an ML
-
learning, deep learning and relevant software framework (R and Python) is highly desired. Very good oral and written communication skills in English are required. Emphasis will also be given on personal
-
that can be used for training machine learning and deep learning models. You will work in tight collaboration with other researchers in Nijmegen, Delft and at the Hubrecht Institute (van Oudenaarden group
-
. The specific research areas we will explore are + Adaptive scientific deep learning methods for mathematical physics problems governed by partial differential equations (domain decomposition, adaptive quadrature
-
learning’ approaches (such as Deep CNN’s) and ‘unsupervised learning’ approaches (such as reinforcement based learning and generative adversarial networks). Some of the main problems with Second Wave AI